{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Регрессионный анализ на градиентном бустинге для прогнозирования стоимости автомобиля"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сервис по продаже автомобилей с пробегом «Не бит, не крашен» разрабатывает приложение для привлечения новых клиентов. В нём можно быстро узнать рыночную стоимость своего автомобиля. В вашем распоряжении исторические данные: технические характеристики, комплектации и цены автомобилей. Вам нужно построить модель для определения стоимости. \n",
    "\n",
    "Заказчику важны:\n",
    "\n",
    "- качество предсказания;\n",
    "- скорость предсказания;\n",
    "- время обучения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Инструкция по выполнению проекта\n",
    "\n",
    "Чтобы усилить исследование, не ограничивайтесь градиентным бустингом. Попробуйте более простые модели — иногда они работают лучше. Эти редкие случаи легко пропустить, если всегда применять только бустинг.\n",
    "\n",
    "Поэкспериментируйте и сравните характеристики моделей: время обучения, время предсказания, точность результата.\n",
    "\n",
    "**Основные шаги:**\n",
    "\n",
    "1. Загрузите данные, путь к файлу: */datasets/autos.csv*.\n",
    "1. Изучите данные. Заполните пропущенные значения и обработайте аномалии в столбцах. Если среди признаков имеются неинформативные, удалите их.\n",
    "1. Подготовьте выборки для обучения моделей.\n",
    "1. Обучите разные модели, одна из которых — LightGBM, как минимум одна — не бустинг. Для каждой модели попробуйте разные гиперпараметры.\n",
    "1. Проанализируйте время обучения, время предсказания и качество моделей.\n",
    "1. Опираясь на критерии заказчика, выберете лучшую модель, проверьте её качество на тестовой выборке.\n",
    "\n",
    "**Примечания:**\n",
    "\n",
    "- Для оценки качества моделей применяйте метрику RMSE.\n",
    "- Значение метрики RMSE должно быть меньше 2500.\n",
    "- Самостоятельно освойте библиотеку LightGBM и её средствами постройте модели градиентного бустинга.\n",
    "- Время выполнения ячейки кода Jupyter Notebook можно получить специальной командой. Найдите её.\n",
    "- Модель градиентного бустинга может долго обучаться, поэтому измените у неё только два-три параметра.\n",
    "- Если перестанет работать Jupyter Notebook, удалите лишние переменные оператором `del`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Описание данных\n",
    "\n",
    "Данные находятся в файле */datasets/autos.csv*.\n",
    "\n",
    "**Признаки**\n",
    "\n",
    "1. `DateCrawled` — дата скачивания анкеты из базы\n",
    "1. `VehicleType` — тип автомобильного кузова\n",
    "1. `RegistrationYear` — год регистрации автомобиля\n",
    "1. `Gearbox` — тип коробки передач\n",
    "1. `Power` — мощность (л. с.)\n",
    "1. `Model` — модель автомобиля\n",
    "1. `Kilometer` — пробег (км)\n",
    "1. `RegistrationMonth` — месяц регистрации автомобиля\n",
    "1. `FuelType` — тип топлива\n",
    "1. `Brand` — марка автомобиля\n",
    "1. `Repaired` — была машина в ремонте или нет\n",
    "1. `DateCreated` — дата создания анкеты\n",
    "1. `NumberOfPictures` — количество фотографий автомобиля\n",
    "1. `PostalCode` — почтовый индекс владельца анкеты (пользователя)\n",
    "1. `LastSeen` — дата последней активности пользователя\n",
    "\n",
    "**Целевой признак**\n",
    "\n",
    "1. `Price` — цена (евро)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Содержание<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Инструкция-по-выполнению-проекта\" data-toc-modified-id=\"Инструкция-по-выполнению-проекта-0.1\"><span class=\"toc-item-num\">0.1&nbsp;&nbsp;</span>Инструкция по выполнению проекта</a></span></li><li><span><a href=\"#Описание-данных\" data-toc-modified-id=\"Описание-данных-0.2\"><span class=\"toc-item-num\">0.2&nbsp;&nbsp;</span>Описание данных</a></span></li></ul></li><li><span><a href=\"#Подготовка-данных\" data-toc-modified-id=\"Подготовка-данных-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Подготовка данных</a></span><ul class=\"toc-item\"><li><span><a href=\"#Настройка-тетради\" data-toc-modified-id=\"Настройка-тетради-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Настройка тетради</a></span></li><li><span><a href=\"#Загрузка-и-изучение-данных\" data-toc-modified-id=\"Загрузка-и-изучение-данных-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Загрузка и изучение данных</a></span></li><li><span><a href=\"#Предобработка-данных\" data-toc-modified-id=\"Предобработка-данных-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Предобработка данных</a></span></li><li><span><a href=\"#Проверка-результатов-предобработкаи-данных\" data-toc-modified-id=\"Проверка-результатов-предобработкаи-данных-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Проверка результатов предобработкаи данных</a></span></li></ul></li><li><span><a href=\"#Обучение-моделей\" data-toc-modified-id=\"Обучение-моделей-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Обучение моделей</a></span><ul class=\"toc-item\"><li><span><a href=\"#Полезные-функции-подготовки-данных-и-подбора-моделей-и-их-параметров\" data-toc-modified-id=\"Полезные-функции-подготовки-данных-и-подбора-моделей-и-их-параметров-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Полезные функции подготовки данных и подбора моделей и их параметров</a></span></li><li><span><a href=\"#Функции-моделей\" data-toc-modified-id=\"Функции-моделей-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Функции моделей</a></span></li><li><span><a href=\"#Применение-функций\" data-toc-modified-id=\"Применение-функций-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Применение функций</a></span></li><li><span><a href=\"#Выбор-лучшей-модели\" data-toc-modified-id=\"Выбор-лучшей-модели-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Выбор лучшей модели</a></span></li></ul></li><li><span><a href=\"#Анализ-моделей\" data-toc-modified-id=\"Анализ-моделей-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Анализ моделей</a></span></li><li><span><a href=\"#Выводы-проекта\" data-toc-modified-id=\"Выводы-проекта-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Выводы проекта</a></span></li><li><span><a href=\"#Чек-лист-проверки\" data-toc-modified-id=\"Чек-лист-проверки-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Чек-лист проверки</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Настройка тетради"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting category_encoders\n",
      "  Downloading category_encoders-2.6.2-py2.py3-none-any.whl (81 kB)\n",
      "\u001b[K     |████████████████████████████████| 81 kB 1.1 MB/s eta 0:00:011\n",
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      "Installing collected packages: category-encoders\n",
      "Successfully installed category-encoders-2.6.2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
      "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting phik\n",
      "  Downloading phik-0.12.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (679 kB)\n",
      "\u001b[K     |████████████████████████████████| 679 kB 953 kB/s eta 0:00:01\n",
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      "Installing collected packages: phik\n",
      "Successfully installed phik-0.12.3\n"
     ]
    }
   ],
   "source": [
    "# Базовые библиотеки\n",
    "import pandas as pd # Датафреймы\n",
    "import numpy as np # Математика для массивов\n",
    "from math import factorial # Факториалы\n",
    "from scipy import stats as st # Статистика\n",
    "import os # Библиотека для оптимизации чтения данных из файла\n",
    "import time # Расчет времени выполнения функций\n",
    "\n",
    "# Pipeline (пайплайн)\n",
    "from sklearn.pipeline import(\n",
    "    Pipeline, # Pipeline с ручным вводом названий шагов.\n",
    "    make_pipeline # Pipeline с автоматическим названием шагов.\n",
    ")\n",
    "# Функция для поддержки экспериментальной функции HavingGridSearchSV\n",
    "from sklearn.experimental import enable_halving_search_cv\n",
    "# Ускоренная автоматизация поиска лучших моделей и их параметров\n",
    "from sklearn.model_selection import HalvingGridSearchCV\n",
    "# Ускоренная автоматизация рандомного поиска лучших моделей и их параметров\n",
    "from sklearn.model_selection import HalvingRandomSearchCV\n",
    "\n",
    "# Автоматизация раздельного декодирования признаков\n",
    "from sklearn.compose import(\n",
    "    make_column_selector, \n",
    "    make_column_transformer, \n",
    "    ColumnTransformer\n",
    ")\n",
    "\n",
    "# Обработка данных для машинного обучения\n",
    "# Стандартизация данных\n",
    "import re\n",
    "#! pip install sklearn.preprocessing \n",
    "from sklearn.preprocessing import(\n",
    "    OneHotEncoder, # Создание отдельных столбцов для каждого категориального значения, drop='first' (удаление первого столбца против dummy-ловушки), sparse=False (?)\n",
    "    OrdinalEncoder, # Кодирование порядковых категориальных признаков\n",
    "    #TargetEncoder, # Кодирование категорий на основе таргетов (ошибка, модуль не найден) \n",
    "    LabelEncoder, \n",
    "    StandardScaler, \n",
    "    MinMaxScaler\n",
    ")\n",
    "# Кодирование категорий на основе таргетов \n",
    "!pip install -U category_encoders\n",
    "from category_encoders.target_encoder import TargetEncoder\n",
    "# Другие функции предобработки данных\n",
    "from sklearn.impute import KNNImputer # Заполнение пропусков в данных методом k-блжиайших соседей.\n",
    "from sklearn.utils import shuffle # Перемешивание данных для уравновешивания их в разных выборках\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor # Коэффициент инфляции дисперсии (5 и более - признак коррелирует со всеми остальными, его можно удалить и выразить через другие признаки)\n",
    "from sklearn.model_selection import(\n",
    "    GridSearchCV, # Поиск гиперпараметров по сетке (GridSearch)\n",
    "    train_test_split, # Разделение выборок с целевыми и нецелевыми признаками на обучающую и тестовую\n",
    "    validation_curve, \n",
    "    StratifiedKFold, # Кроссвалидация с указанием количества фолдов (частей, на которые будет разбита обучающая выборка, одна из которых будет участвовать в валидации)\n",
    "    KFold, # Кроссвалидация \n",
    "    cross_val_score # Оценка качества модели на кроссвалидации\n",
    ")\n",
    "\n",
    "# Различные модели машинного обучения (в данном проекте требуется регрессия)\n",
    "# (есть разбор на https://russianblogs.com/article/83691573909/)\n",
    "# Линейная модель\n",
    "from sklearn.linear_model import(\n",
    "    #LogisticRegression, # Линейная классификация\n",
    "    LinearRegression, # Линейная регрессия\n",
    "    Ridge , # Линейная регрессия. \"Хребтовая\" регрессия (метод наименьших квадратов)\n",
    "    BayesianRidge , # Линейная регрессия. Байесовская \"хребтовая\" регрессия (максимизации предельного логарифмического правдоподобия)\n",
    "    SGDRegressor # Линейная регрессия. SGD - Стохастический градиентный спуск (минимизирует регуляризованные эмпирические потери за счет стохастического градиентного спуска)\n",
    ")\n",
    "# Решающее дерево\n",
    "from sklearn.tree import(\n",
    "    #DecisionTreeClassifier, # Решающее дерево. Классификация\n",
    "    DecisionTreeRegressor # Решающее дерево. Регрессия\n",
    ")\n",
    "# Случайный лес\n",
    "from sklearn.ensemble import(\n",
    "    #RandomForestClassifier, # Случайный лес. Классификация\n",
    "    RandomForestRegressor # Случайный лес. Регрессия\n",
    ")\n",
    "# Машина опорных векторов\n",
    "from sklearn.svm import(\n",
    "    SVR # # Линейная модель. Регрессия с использованием опорных векторов\n",
    ")\n",
    "# Нейронная сеть\n",
    "from sklearn.neural_network import(\n",
    "    MLPRegressor # Нейронная сеть. Регрессия\n",
    ")\n",
    "# CatBoost (made in Yandex)\n",
    "from catboost import(\n",
    "    CatBoostRegressor # CatBoost (Яндекс). Регрессия\n",
    ")\n",
    "# LightGBM\n",
    "from lightgbm import(\n",
    "    LGBMRegressor # LightGBM. Регрессия\n",
    ")\n",
    "\n",
    "# Метрики (Показатели качества моделей)\n",
    "from sklearn.metrics import(\n",
    "    # Метрики для моделей регрессии\n",
    "    mean_absolute_error, # MAE, Средняя абсолютная ошибка (не чувствительная к выбросам)\n",
    "    mean_absolute_percentage_error, # MAPE, Средняя абсолютная ошибка в % (универсальная в %)\n",
    "    mean_squared_error, # MSE, Средняя квадратичная ошибка (дисперсия, чувствительная к выбросам), RMSE (сигма) = mean_squared_error(test_y, preds, squared=False)\n",
    "    r2_score, # R^2, Коэффициент детерминации (универсальная в %, чувствительная к выбросам, может быть отрицательной и возвращать NaN)\n",
    "    \n",
    "    # Другое\n",
    "    make_scorer, # Функция для использования собственных функций в параметре scoring функции HalvingGridSearchCV\n",
    "    ConfusionMatrixDisplay\n",
    ")\n",
    "\n",
    "# Визуализация графиков\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import rcParams, rcParamsDefault\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "# Для поиска совпадений \n",
    "# в названиях населённых пунктов\n",
    "from fuzzywuzzy import fuzz\n",
    "from fuzzywuzzy import process\n",
    "\n",
    "# Улучшенная функция \n",
    "# определения корреляции\n",
    "# (возвращает сообщение о том, \n",
    "# что модуль не найден) \n",
    "!pip3 install phik\n",
    "import phik \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Отображение всех столбцов таблицы\n",
    "pd.set_option('display.max_columns', None)\n",
    "# Обязательно для нормального отображения графиков plt\n",
    "rcParams['figure.figsize'] = 10, 6\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "# Дополнительно и не обязательно для декорирования графиков\n",
    "factor = .8\n",
    "default_dpi = rcParamsDefault['figure.dpi']\n",
    "rcParams['figure.dpi'] = default_dpi * factor\n",
    "\n",
    "# Глобальная переменная \n",
    "# для функций со случайными значениями\n",
    "STATE = 42\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Загрузка и изучение данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Загрузка данных\n",
    "def read_csv_file(path1, path2):\n",
    "    if os.path.exists(path1):\n",
    "        data = pd.read_csv(path1)\n",
    "    elif os.path.exists(path2):\n",
    "        data = pd.read_csv(path2)\n",
    "    else:\n",
    "        print('Файл не найден')\n",
    "    return data\n",
    "\n",
    "data = read_csv_file(\n",
    "    '/datasets/autos.csv', \n",
    "    'datasets/autos.csv'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 354369 entries, 0 to 354368\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count   Dtype \n",
      "---  ------             --------------   ----- \n",
      " 0   DateCrawled        354369 non-null  object\n",
      " 1   Price              354369 non-null  int64 \n",
      " 2   VehicleType        316879 non-null  object\n",
      " 3   RegistrationYear   354369 non-null  int64 \n",
      " 4   Gearbox            334536 non-null  object\n",
      " 5   Power              354369 non-null  int64 \n",
      " 6   Model              334664 non-null  object\n",
      " 7   Kilometer          354369 non-null  int64 \n",
      " 8   RegistrationMonth  354369 non-null  int64 \n",
      " 9   FuelType           321474 non-null  object\n",
      " 10  Brand              354369 non-null  object\n",
      " 11  Repaired           283215 non-null  object\n",
      " 12  DateCreated        354369 non-null  object\n",
      " 13  NumberOfPictures   354369 non-null  int64 \n",
      " 14  PostalCode         354369 non-null  int64 \n",
      " 15  LastSeen           354369 non-null  object\n",
      "dtypes: int64(7), object(9)\n",
      "memory usage: 43.3+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateCrawled</th>\n",
       "      <th>Price</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>RegistrationYear</th>\n",
       "      <th>Gearbox</th>\n",
       "      <th>Power</th>\n",
       "      <th>Model</th>\n",
       "      <th>Kilometer</th>\n",
       "      <th>RegistrationMonth</th>\n",
       "      <th>FuelType</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Repaired</th>\n",
       "      <th>DateCreated</th>\n",
       "      <th>NumberOfPictures</th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>LastSeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-24 11:52:17</td>\n",
       "      <td>480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>2016-04-07 03:16:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-24 10:58:45</td>\n",
       "      <td>18300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manual</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>audi</td>\n",
       "      <td>yes</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>2016-04-07 01:46:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-14 12:52:21</td>\n",
       "      <td>9800</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>auto</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>2016-04-05 12:47:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-17 16:54:04</td>\n",
       "      <td>1500</td>\n",
       "      <td>small</td>\n",
       "      <td>2001</td>\n",
       "      <td>manual</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-17 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>2016-03-17 17:40:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-31 17:25:20</td>\n",
       "      <td>3600</td>\n",
       "      <td>small</td>\n",
       "      <td>2008</td>\n",
       "      <td>manual</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>skoda</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-31 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>2016-04-06 10:17:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-04-04 17:36:23</td>\n",
       "      <td>650</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1995</td>\n",
       "      <td>manual</td>\n",
       "      <td>102</td>\n",
       "      <td>3er</td>\n",
       "      <td>150000</td>\n",
       "      <td>10</td>\n",
       "      <td>petrol</td>\n",
       "      <td>bmw</td>\n",
       "      <td>yes</td>\n",
       "      <td>2016-04-04 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>33775</td>\n",
       "      <td>2016-04-06 19:17:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-04-01 20:48:51</td>\n",
       "      <td>2200</td>\n",
       "      <td>convertible</td>\n",
       "      <td>2004</td>\n",
       "      <td>manual</td>\n",
       "      <td>109</td>\n",
       "      <td>2_reihe</td>\n",
       "      <td>150000</td>\n",
       "      <td>8</td>\n",
       "      <td>petrol</td>\n",
       "      <td>peugeot</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>67112</td>\n",
       "      <td>2016-04-05 18:18:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-03-21 18:54:38</td>\n",
       "      <td>0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1980</td>\n",
       "      <td>manual</td>\n",
       "      <td>50</td>\n",
       "      <td>other</td>\n",
       "      <td>40000</td>\n",
       "      <td>7</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-21 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>19348</td>\n",
       "      <td>2016-03-25 16:47:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-04-04 23:42:13</td>\n",
       "      <td>14500</td>\n",
       "      <td>bus</td>\n",
       "      <td>2014</td>\n",
       "      <td>manual</td>\n",
       "      <td>125</td>\n",
       "      <td>c_max</td>\n",
       "      <td>30000</td>\n",
       "      <td>8</td>\n",
       "      <td>petrol</td>\n",
       "      <td>ford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-04 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>94505</td>\n",
       "      <td>2016-04-04 23:42:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-03-17 10:53:50</td>\n",
       "      <td>999</td>\n",
       "      <td>small</td>\n",
       "      <td>1998</td>\n",
       "      <td>manual</td>\n",
       "      <td>101</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-17 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>27472</td>\n",
       "      <td>2016-03-31 17:17:06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           DateCrawled  Price  VehicleType  RegistrationYear Gearbox  Power  \\\n",
       "0  2016-03-24 11:52:17    480          NaN              1993  manual      0   \n",
       "1  2016-03-24 10:58:45  18300        coupe              2011  manual    190   \n",
       "2  2016-03-14 12:52:21   9800          suv              2004    auto    163   \n",
       "3  2016-03-17 16:54:04   1500        small              2001  manual     75   \n",
       "4  2016-03-31 17:25:20   3600        small              2008  manual     69   \n",
       "5  2016-04-04 17:36:23    650        sedan              1995  manual    102   \n",
       "6  2016-04-01 20:48:51   2200  convertible              2004  manual    109   \n",
       "7  2016-03-21 18:54:38      0        sedan              1980  manual     50   \n",
       "8  2016-04-04 23:42:13  14500          bus              2014  manual    125   \n",
       "9  2016-03-17 10:53:50    999        small              1998  manual    101   \n",
       "\n",
       "     Model  Kilometer  RegistrationMonth  FuelType       Brand Repaired  \\\n",
       "0     golf     150000                  0    petrol  volkswagen      NaN   \n",
       "1      NaN     125000                  5  gasoline        audi      yes   \n",
       "2    grand     125000                  8  gasoline        jeep      NaN   \n",
       "3     golf     150000                  6    petrol  volkswagen       no   \n",
       "4    fabia      90000                  7  gasoline       skoda       no   \n",
       "5      3er     150000                 10    petrol         bmw      yes   \n",
       "6  2_reihe     150000                  8    petrol     peugeot       no   \n",
       "7    other      40000                  7    petrol  volkswagen       no   \n",
       "8    c_max      30000                  8    petrol        ford      NaN   \n",
       "9     golf     150000                  0       NaN  volkswagen      NaN   \n",
       "\n",
       "           DateCreated  NumberOfPictures  PostalCode             LastSeen  \n",
       "0  2016-03-24 00:00:00                 0       70435  2016-04-07 03:16:57  \n",
       "1  2016-03-24 00:00:00                 0       66954  2016-04-07 01:46:50  \n",
       "2  2016-03-14 00:00:00                 0       90480  2016-04-05 12:47:46  \n",
       "3  2016-03-17 00:00:00                 0       91074  2016-03-17 17:40:17  \n",
       "4  2016-03-31 00:00:00                 0       60437  2016-04-06 10:17:21  \n",
       "5  2016-04-04 00:00:00                 0       33775  2016-04-06 19:17:07  \n",
       "6  2016-04-01 00:00:00                 0       67112  2016-04-05 18:18:39  \n",
       "7  2016-03-21 00:00:00                 0       19348  2016-03-25 16:47:58  \n",
       "8  2016-04-04 00:00:00                 0       94505  2016-04-04 23:42:13  \n",
       "9  2016-03-17 00:00:00                 0       27472  2016-03-31 17:17:06  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Первичный анализ данных\n",
    "print(data.info()) \n",
    "data.head(10) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>RegistrationYear</th>\n",
       "      <th>Power</th>\n",
       "      <th>Kilometer</th>\n",
       "      <th>RegistrationMonth</th>\n",
       "      <th>NumberOfPictures</th>\n",
       "      <th>PostalCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.0</td>\n",
       "      <td>354369.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4416.656776</td>\n",
       "      <td>2004.234448</td>\n",
       "      <td>110.094337</td>\n",
       "      <td>128211.172535</td>\n",
       "      <td>5.714645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50508.689087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4514.158514</td>\n",
       "      <td>90.227958</td>\n",
       "      <td>189.850405</td>\n",
       "      <td>37905.341530</td>\n",
       "      <td>3.726421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25783.096248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1050.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30165.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49413.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6400.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20000.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99998.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price  RegistrationYear          Power      Kilometer  \\\n",
       "count  354369.000000     354369.000000  354369.000000  354369.000000   \n",
       "mean     4416.656776       2004.234448     110.094337  128211.172535   \n",
       "std      4514.158514         90.227958     189.850405   37905.341530   \n",
       "min         0.000000       1000.000000       0.000000    5000.000000   \n",
       "25%      1050.000000       1999.000000      69.000000  125000.000000   \n",
       "50%      2700.000000       2003.000000     105.000000  150000.000000   \n",
       "75%      6400.000000       2008.000000     143.000000  150000.000000   \n",
       "max     20000.000000       9999.000000   20000.000000  150000.000000   \n",
       "\n",
       "       RegistrationMonth  NumberOfPictures     PostalCode  \n",
       "count      354369.000000          354369.0  354369.000000  \n",
       "mean            5.714645               0.0   50508.689087  \n",
       "std             3.726421               0.0   25783.096248  \n",
       "min             0.000000               0.0    1067.000000  \n",
       "25%             3.000000               0.0   30165.000000  \n",
       "50%             6.000000               0.0   49413.000000  \n",
       "75%             9.000000               0.0   71083.000000  \n",
       "max            12.000000               0.0   99998.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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     "metadata": {
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   ],
   "source": [
    "# Анализ значений датафрейма\n",
    "data.hist() \n",
    "plt.subplots_adjust(wspace=.4, hspace=.5)\n",
    "\n",
    "data.describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Всего объектов: 354369\n",
      "\n",
      "Количество объектов с пропусками в признаках:\n",
      "VehicleType (object)\t= 37490 (10%)\n",
      "Gearbox (object)\t= 19833 (5%)\n",
      "Model (object)\t= 19705 (5%)\n",
      "FuelType (object)\t= 32895 (9%)\n",
      "Repaired (object)\t= 71154 (20%)\n"
     ]
    }
   ],
   "source": [
    "# Подсчёт пропусков\n",
    "data_shape = data.shape[0]\n",
    "print('Всего объектов:', data_shape) \n",
    "print() \n",
    "print('Количество объектов с пропусками в признаках:') \n",
    "for i in data.columns:\n",
    "    if data_shape - data[i].loc[data[i].notna()].shape[0] > 0:\n",
    "        _a = data_shape - data[i].loc[data[i].notna()].shape[0]\n",
    "        _b = int((1 - data[i].loc[data[i].notna()].shape[0] / data_shape) * 100) \n",
    "        _c = data[i].dtype\n",
    "        print(f'{i} ({_c})\\t= {_a} ({_b}%)') \n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Уникальные значения атрибута \"RegistrationYear\":\n",
      "[1000 1001 1039 1111 1200 1234 1253 1255 1300 1400 1500 1600 1602 1688\n",
      " 1800 1910 1915 1919 1920 1923 1925 1927 1928 1929 1930 1931 1932 1933\n",
      " 1934 1935 1936 1937 1938 1940 1941 1942 1943 1944 1945 1946 1947 1948\n",
      " 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962\n",
      " 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976\n",
      " 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990\n",
      " 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004\n",
      " 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018\n",
      " 2019 2066 2200 2222 2290 2500 2800 2900 3000 3200 3500 3700 3800 4000\n",
      " 4100 4500 4800 5000 5300 5555 5600 5900 5911 6000 6500 7000 7100 7500\n",
      " 7800 8000 8200 8455 8500 8888 9000 9229 9450 9996 9999]\n",
      "\n",
      "Количество значений атрибута \"RegistrationYear\", которые меньше 1990 и больше 2023: 171 это 0.048254785266205566 %\n"
     ]
    }
   ],
   "source": [
    "# Анализ значений атрибута \"RegistrationYear\" \n",
    "print('Уникальные значения атрибута \"RegistrationYear\":') \n",
    "print(np.sort(data['RegistrationYear'].unique())) \n",
    "print() \n",
    "print('Количество значений атрибута \"RegistrationYear\", которые меньше 1990 и больше 2023:',  \n",
    "      data.loc[\n",
    "          (data['RegistrationYear'] < 1900) |\n",
    "          (data['RegistrationYear'] > 2023), \n",
    "          'RegistrationYear'\n",
    "      ].count(), \n",
    "      'это', \n",
    "      (data.loc[\n",
    "          (data['RegistrationYear'] < 1900) |\n",
    "          (data['RegistrationYear'] > 2023), \n",
    "          'RegistrationYear'\n",
    "      ].count() / data_shape) * 100, '%' \n",
    "     ) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40225 объектов имеют мощность двигателя меньше 0.75 л.с. Это 11.351162206626427% от всего количества объектов.  \n",
      "\n",
      "Примеры подобных объектов:\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Анализ минимальна значений атрибута \"Power\" \n",
    "string = f\"{data.loc[data['Power'] < .75, 'Power'].count()} объектов имеют мощность двигателя меньше 0.75 л.с. \"\n",
    "string += f\"Это {data.loc[data['Power'] < .75, 'Power'].count() / data_shape * 100}% от всего количества объектов. \" \n",
    "print(string, '\\n')\n",
    "\n",
    "print('Примеры подобных объектов:') \n",
    "data.loc[data['Power'] < .75].head() \n",
    "\n",
    "print() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82 объектов имеют мощность двигателя больше 5000 л.с. Это 0.023139721589642434% от всего количества объектов.  \n",
      "\n",
      "Примеры подобных объектов:\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateCrawled</th>\n",
       "      <th>Price</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>RegistrationYear</th>\n",
       "      <th>Gearbox</th>\n",
       "      <th>Power</th>\n",
       "      <th>Model</th>\n",
       "      <th>Kilometer</th>\n",
       "      <th>RegistrationMonth</th>\n",
       "      <th>FuelType</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Repaired</th>\n",
       "      <th>DateCreated</th>\n",
       "      <th>NumberOfPictures</th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>LastSeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>2016-04-02 19:25:25</td>\n",
       "      <td>1499</td>\n",
       "      <td>small</td>\n",
       "      <td>1999</td>\n",
       "      <td>manual</td>\n",
       "      <td>7515</td>\n",
       "      <td>lupo</td>\n",
       "      <td>150000</td>\n",
       "      <td>4</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-02 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>65830</td>\n",
       "      <td>2016-04-06 11:46:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11039</th>\n",
       "      <td>2016-03-25 19:55:32</td>\n",
       "      <td>0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1998</td>\n",
       "      <td>manual</td>\n",
       "      <td>10317</td>\n",
       "      <td>other</td>\n",
       "      <td>150000</td>\n",
       "      <td>8</td>\n",
       "      <td>petrol</td>\n",
       "      <td>fiat</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-25 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>57520</td>\n",
       "      <td>2016-04-01 19:16:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25232</th>\n",
       "      <td>2016-03-28 19:57:39</td>\n",
       "      <td>10900</td>\n",
       "      <td>bus</td>\n",
       "      <td>2009</td>\n",
       "      <td>manual</td>\n",
       "      <td>10520</td>\n",
       "      <td>caddy</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-28 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>36272</td>\n",
       "      <td>2016-04-07 02:47:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33952</th>\n",
       "      <td>2016-03-09 11:37:03</td>\n",
       "      <td>3740</td>\n",
       "      <td>small</td>\n",
       "      <td>2006</td>\n",
       "      <td>manual</td>\n",
       "      <td>6920</td>\n",
       "      <td>aygo</td>\n",
       "      <td>90000</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>toyota</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-09 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>94116</td>\n",
       "      <td>2016-03-17 05:16:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44520</th>\n",
       "      <td>2016-03-10 22:37:21</td>\n",
       "      <td>2500</td>\n",
       "      <td>convertible</td>\n",
       "      <td>1998</td>\n",
       "      <td>manual</td>\n",
       "      <td>7512</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-10 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>68239</td>\n",
       "      <td>2016-04-05 15:17:50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               DateCrawled  Price  VehicleType  RegistrationYear Gearbox  \\\n",
       "7661   2016-04-02 19:25:25   1499        small              1999  manual   \n",
       "11039  2016-03-25 19:55:32      0        sedan              1998  manual   \n",
       "25232  2016-03-28 19:57:39  10900          bus              2009  manual   \n",
       "33952  2016-03-09 11:37:03   3740        small              2006  manual   \n",
       "44520  2016-03-10 22:37:21   2500  convertible              1998  manual   \n",
       "\n",
       "       Power  Model  Kilometer  RegistrationMonth  FuelType       Brand  \\\n",
       "7661    7515   lupo     150000                  4    petrol  volkswagen   \n",
       "11039  10317  other     150000                  8    petrol        fiat   \n",
       "25232  10520  caddy     150000                  6  gasoline  volkswagen   \n",
       "33952   6920   aygo      90000                 10       NaN      toyota   \n",
       "44520   7512   golf     150000                  6       NaN  volkswagen   \n",
       "\n",
       "      Repaired          DateCreated  NumberOfPictures  PostalCode  \\\n",
       "7661       NaN  2016-04-02 00:00:00                 0       65830   \n",
       "11039       no  2016-03-25 00:00:00                 0       57520   \n",
       "25232       no  2016-03-28 00:00:00                 0       36272   \n",
       "33952       no  2016-03-09 00:00:00                 0       94116   \n",
       "44520      NaN  2016-03-10 00:00:00                 0       68239   \n",
       "\n",
       "                  LastSeen  \n",
       "7661   2016-04-06 11:46:49  \n",
       "11039  2016-04-01 19:16:33  \n",
       "25232  2016-04-07 02:47:02  \n",
       "33952  2016-03-17 05:16:32  \n",
       "44520  2016-04-05 15:17:50  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Анализ максимальная значений атрибута \"Power\" \n",
    "string = f\"{data.loc[data['Power'] > 5000, 'Power'].count()} объектов имеют мощность двигателя больше 5000 л.с. \"\n",
    "string += f\"Это {data.loc[data['Power'] > 5000, 'Power'].count() / data_shape * 100}% от всего количества объектов. \"\n",
    "print(string, '\\n')\n",
    "\n",
    "print('Примеры подобных объектов:') \n",
    "data.loc[data['Power'] > 5000].head() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Уникальные значения признака \"DateCrawled\": ['2016-03-24 11:52:17' '2016-03-24 10:58:45' '2016-03-14 12:52:21' ...\n",
      " '2016-03-21 09:50:58' '2016-03-14 17:48:27' '2016-03-19 18:57:12']\n",
      "Всего унимальных значений признака \"DateCrawled\": 271174\n",
      "\n",
      "Уникальные значения признака \"VehicleType\": [nan 'coupe' 'suv' 'small' 'sedan' 'convertible' 'bus' 'wagon' 'other']\n",
      "Всего унимальных значений признака \"VehicleType\": 9\n",
      "\n",
      "Уникальные значения признака \"Gearbox\": ['manual' 'auto' nan]\n",
      "Всего унимальных значений признака \"Gearbox\": 3\n",
      "\n",
      "Уникальные значения признака \"Model\": ['golf' nan 'grand' 'fabia' '3er' '2_reihe' 'other' 'c_max' '3_reihe'\n",
      " 'passat' 'navara' 'ka' 'polo' 'twingo' 'a_klasse' 'scirocco' '5er'\n",
      " 'meriva' 'arosa' 'c4' 'civic' 'transporter' 'punto' 'e_klasse' 'clio'\n",
      " 'kadett' 'kangoo' 'corsa' 'one' 'fortwo' '1er' 'b_klasse' 'signum'\n",
      " 'astra' 'a8' 'jetta' 'fiesta' 'c_klasse' 'micra' 'vito' 'sprinter' '156'\n",
      " 'escort' 'forester' 'xc_reihe' 'scenic' 'a4' 'a1' 'insignia' 'combo'\n",
      " 'focus' 'tt' 'a6' 'jazz' 'omega' 'slk' '7er' '80' '147' '100' 'z_reihe'\n",
      " 'sportage' 'sorento' 'v40' 'ibiza' 'mustang' 'eos' 'touran' 'getz' 'a3'\n",
      " 'almera' 'megane' 'lupo' 'r19' 'zafira' 'caddy' 'mondeo' 'cordoba' 'colt'\n",
      " 'impreza' 'vectra' 'berlingo' 'tiguan' 'i_reihe' 'espace' 'sharan'\n",
      " '6_reihe' 'panda' 'up' 'seicento' 'ceed' '5_reihe' 'yeti' 'octavia' 'mii'\n",
      " 'rx_reihe' '6er' 'modus' 'fox' 'matiz' 'beetle' 'c1' 'rio' 'touareg'\n",
      " 'logan' 'spider' 'cuore' 's_max' 'a2' 'galaxy' 'c3' 'viano' 's_klasse'\n",
      " '1_reihe' 'avensis' 'roomster' 'sl' 'kaefer' 'santa' 'cooper' 'leon'\n",
      " '4_reihe' 'a5' '500' 'laguna' 'ptcruiser' 'clk' 'primera' 'x_reihe'\n",
      " 'exeo' '159' 'transit' 'juke' 'qashqai' 'carisma' 'accord' 'corolla'\n",
      " 'lanos' 'phaeton' 'verso' 'swift' 'rav' 'picanto' 'boxster' 'kalos'\n",
      " 'superb' 'stilo' 'alhambra' 'mx_reihe' 'roadster' 'ypsilon' 'cayenne'\n",
      " 'galant' 'justy' '90' 'sirion' 'crossfire' 'agila' 'duster' 'cr_reihe'\n",
      " 'v50' 'c_reihe' 'v_klasse' 'm_klasse' 'yaris' 'c5' 'aygo' 'cc' 'carnival'\n",
      " 'fusion' '911' 'bora' 'forfour' 'm_reihe' 'cl' 'tigra' '300c' 'spark'\n",
      " 'v70' 'kuga' 'x_type' 'ducato' 's_type' 'x_trail' 'toledo' 'altea'\n",
      " 'voyager' 'calibra' 'bravo' 'antara' 'tucson' 'citigo' 'jimny' 'wrangler'\n",
      " 'lybra' 'q7' 'lancer' 'captiva' 'c2' 'discovery' 'freelander' 'sandero'\n",
      " 'note' '900' 'cherokee' 'clubman' 'samara' 'defender' '601' 'cx_reihe'\n",
      " 'legacy' 'pajero' 'auris' 'niva' 's60' 'nubira' 'vivaro' 'g_klasse'\n",
      " 'lodgy' '850' 'range_rover' 'q3' 'serie_2' 'glk' 'charade' 'croma'\n",
      " 'outlander' 'doblo' 'musa' 'move' '9000' 'v60' '145' 'aveo' '200' 'b_max'\n",
      " 'range_rover_sport' 'terios' 'rangerover' 'q5' 'range_rover_evoque'\n",
      " 'materia' 'delta' 'gl' 'kalina' 'amarok' 'elefantino' 'i3' 'kappa'\n",
      " 'serie_3' 'serie_1']\n",
      "Всего унимальных значений признака \"Model\": 251\n",
      "\n",
      "Уникальные значения признака \"FuelType\": ['petrol' 'gasoline' nan 'lpg' 'other' 'hybrid' 'cng' 'electric']\n",
      "Всего унимальных значений признака \"FuelType\": 8\n",
      "\n",
      "Уникальные значения признака \"Brand\": ['volkswagen' 'audi' 'jeep' 'skoda' 'bmw' 'peugeot' 'ford' 'mazda'\n",
      " 'nissan' 'renault' 'mercedes_benz' 'opel' 'seat' 'citroen' 'honda' 'fiat'\n",
      " 'mini' 'smart' 'hyundai' 'sonstige_autos' 'alfa_romeo' 'subaru' 'volvo'\n",
      " 'mitsubishi' 'kia' 'suzuki' 'lancia' 'toyota' 'chevrolet' 'dacia'\n",
      " 'daihatsu' 'trabant' 'saab' 'chrysler' 'jaguar' 'daewoo' 'porsche'\n",
      " 'rover' 'land_rover' 'lada']\n",
      "Всего унимальных значений признака \"Brand\": 40\n",
      "\n",
      "Уникальные значения признака \"Repaired\": [nan 'yes' 'no']\n",
      "Всего унимальных значений признака \"Repaired\": 3\n",
      "\n",
      "Уникальные значения признака \"DateCreated\": ['2016-03-24 00:00:00' '2016-03-14 00:00:00' '2016-03-17 00:00:00'\n",
      " '2016-03-31 00:00:00' '2016-04-04 00:00:00' '2016-04-01 00:00:00'\n",
      " '2016-03-21 00:00:00' '2016-03-26 00:00:00' '2016-04-07 00:00:00'\n",
      " '2016-03-15 00:00:00' '2016-03-11 00:00:00' '2016-03-20 00:00:00'\n",
      " '2016-03-23 00:00:00' '2016-03-27 00:00:00' '2016-03-12 00:00:00'\n",
      " '2016-03-13 00:00:00' '2016-03-18 00:00:00' '2016-03-10 00:00:00'\n",
      " '2016-03-07 00:00:00' '2016-03-09 00:00:00' '2016-03-08 00:00:00'\n",
      " '2016-04-03 00:00:00' '2016-03-29 00:00:00' '2016-03-25 00:00:00'\n",
      " '2016-03-28 00:00:00' '2016-03-30 00:00:00' '2016-03-22 00:00:00'\n",
      " '2016-02-09 00:00:00' '2016-03-05 00:00:00' '2016-04-02 00:00:00'\n",
      " '2016-03-16 00:00:00' '2016-03-19 00:00:00' '2016-04-05 00:00:00'\n",
      " '2016-03-06 00:00:00' '2016-02-12 00:00:00' '2016-03-03 00:00:00'\n",
      " '2016-03-01 00:00:00' '2016-03-04 00:00:00' '2016-04-06 00:00:00'\n",
      " '2016-02-15 00:00:00' '2016-02-24 00:00:00' '2016-02-27 00:00:00'\n",
      " '2015-03-20 00:00:00' '2016-02-28 00:00:00' '2016-02-17 00:00:00'\n",
      " '2016-01-27 00:00:00' '2016-02-20 00:00:00' '2016-02-29 00:00:00'\n",
      " '2016-02-10 00:00:00' '2016-02-23 00:00:00' '2016-02-21 00:00:00'\n",
      " '2015-11-02 00:00:00' '2016-02-19 00:00:00' '2016-02-26 00:00:00'\n",
      " '2016-02-11 00:00:00' '2016-01-10 00:00:00' '2016-02-06 00:00:00'\n",
      " '2016-02-18 00:00:00' '2016-01-29 00:00:00' '2016-03-02 00:00:00'\n",
      " '2015-12-06 00:00:00' '2016-01-24 00:00:00' '2016-01-30 00:00:00'\n",
      " '2016-02-02 00:00:00' '2016-02-16 00:00:00' '2016-02-13 00:00:00'\n",
      " '2016-02-05 00:00:00' '2016-02-22 00:00:00' '2015-11-17 00:00:00'\n",
      " '2014-03-10 00:00:00' '2016-02-07 00:00:00' '2016-01-23 00:00:00'\n",
      " '2016-02-25 00:00:00' '2016-02-14 00:00:00' '2016-01-02 00:00:00'\n",
      " '2015-09-04 00:00:00' '2015-11-12 00:00:00' '2015-12-27 00:00:00'\n",
      " '2015-11-24 00:00:00' '2016-01-20 00:00:00' '2016-02-03 00:00:00'\n",
      " '2015-12-05 00:00:00' '2015-08-07 00:00:00' '2016-01-28 00:00:00'\n",
      " '2016-01-31 00:00:00' '2016-02-08 00:00:00' '2016-01-07 00:00:00'\n",
      " '2016-01-22 00:00:00' '2016-01-18 00:00:00' '2016-01-08 00:00:00'\n",
      " '2015-11-23 00:00:00' '2016-01-13 00:00:00' '2016-01-17 00:00:00'\n",
      " '2016-01-15 00:00:00' '2015-11-08 00:00:00' '2016-01-26 00:00:00'\n",
      " '2016-02-04 00:00:00' '2016-01-25 00:00:00' '2016-01-16 00:00:00'\n",
      " '2015-08-10 00:00:00' '2016-01-03 00:00:00' '2016-01-19 00:00:00'\n",
      " '2015-12-30 00:00:00' '2016-02-01 00:00:00' '2015-12-17 00:00:00'\n",
      " '2015-11-10 00:00:00' '2016-01-06 00:00:00' '2015-09-09 00:00:00'\n",
      " '2015-06-18 00:00:00']\n",
      "Всего унимальных значений признака \"DateCreated\": 109\n",
      "\n",
      "Уникальные значения признака \"LastSeen\": ['2016-04-07 03:16:57' '2016-04-07 01:46:50' '2016-04-05 12:47:46' ...\n",
      " '2016-03-19 20:44:43' '2016-03-29 10:17:23' '2016-03-21 10:42:49']\n",
      "Всего унимальных значений признака \"LastSeen\": 179150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Анализ уникальных значений \n",
    "# категориальных текстовых признаков\n",
    "for i in data.select_dtypes(include='object').columns: \n",
    "    print(f'Уникальные значения признака \"{i}\":', data[i].unique()) \n",
    "    print(f'Всего унимальных значений признака \"{i}\":', len(data[i].unique())) \n",
    "    print() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "golf ~ [('golf', 100), ('gl', 67), ('twingo', 60)]\n",
      "no_value ~ [('no_value', 100), ('altea', 60), ('lupo', 51)]\n",
      "grand ~ [('grand', 100), ('panda', 60), ('logan', 60)]\n",
      "fabia ~ [('fabia', 100), ('ibiza', 60), ('agila', 60)]\n",
      "3er ~ [('3er', 100), ('5er', 67), ('1er', 67)]\n",
      "2_reihe ~ [('2_reihe', 100), ('3_reihe', 86), ('z_reihe', 86)]\n",
      "other ~ [('other', 100), ('transporter', 72), ('boxster', 67)]\n",
      "c_max ~ [('c_max', 100), ('s_max', 80), ('b_max', 80)]\n",
      "3_reihe ~ [('3_reihe', 100), ('2_reihe', 86), ('z_reihe', 86)]\n",
      "passat ~ [('passat', 100), ('tt', 60), ('arosa', 55)]\n",
      "navara ~ [('navara', 100), ('rav', 72), ('niva', 68)]\n",
      "ka ~ [('ka', 100), ('kadett', 90), ('kangoo', 90)]\n",
      "polo ~ [('polo', 100), ('doblo', 67), ('toledo', 60)]\n",
      "twingo ~ [('twingo', 100), ('elefantino', 72), ('citigo', 67)]\n",
      "a_klasse ~ [('a_klasse', 100), ('e_klasse', 88), ('b_klasse', 88)]\n",
      "scirocco ~ [('scirocco', 100), ('cc', 90), ('clio', 68)]\n",
      "5er ~ [('5er', 100), ('3er', 67), ('1er', 67)]\n",
      "meriva ~ [('meriva', 100), ('materia', 77), ('niva', 68)]\n",
      "arosa ~ [('arosa', 100), ('carisma', 67), ('corsa', 60)]\n",
      "c4 ~ [('c4', 100), ('300c', 60), ('a4', 50)]\n",
      "civic ~ [('civic', 100), ('mii', 60), ('cc', 57)]\n",
      "transporter ~ [('transporter', 100), ('other', 72), ('note', 68)]\n",
      "punto ~ [('punto', 100), ('picanto', 67), ('ducato', 55)]\n",
      "e_klasse ~ [('e_klasse', 100), ('a_klasse', 88), ('b_klasse', 88)]\n",
      "clio ~ [('clio', 100), ('cl', 90), ('scirocco', 68)]\n",
      "kadett ~ [('kadett', 100), ('ka', 90), ('tt', 90)]\n",
      "kangoo ~ [('kangoo', 100), ('ka', 90), ('aygo', 68)]\n",
      "corsa ~ [('corsa', 100), ('cordoba', 67), ('carisma', 67)]\n",
      "one ~ [('one', 100), ('phaeton', 72), ('ypsilon', 72)]\n",
      "fortwo ~ [('fortwo', 100), ('sorento', 62), ('forfour', 62)]\n",
      "1er ~ [('1er', 100), ('3er', 67), ('5er', 67)]\n",
      "b_klasse ~ [('b_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]\n",
      "signum ~ [('signum', 100), ('insignia', 57), ('tiguan', 50)]\n",
      "astra ~ [('astra', 100), ('antara', 73), ('rav', 72)]\n",
      "a8 ~ [('a8', 100), ('meriva', 60), ('corsa', 60)]\n",
      "jetta ~ [('jetta', 100), ('tt', 90), ('a8', 60)]\n",
      "fiesta ~ [('fiesta', 100), ('a8', 60), ('a4', 60)]\n",
      "c_klasse ~ [('c_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]\n",
      "micra ~ [('micra', 100), ('rav', 72), ('corsa', 60)]\n",
      "vito ~ [('vito', 100), ('viano', 67), ('citigo', 60)]\n",
      "sprinter ~ [('sprinter', 100), ('spider', 71), ('note', 68)]\n",
      "156 ~ [('156', 100), ('159', 67), ('145', 67)]\n",
      "escort ~ [('escort', 100), ('colt', 68), ('sorento', 62)]\n",
      "forester ~ [('forester', 100), ('boxster', 67), ('other', 62)]\n",
      "xc_reihe ~ [('xc_reihe', 100), ('x_reihe', 93), ('c_reihe', 93)]\n",
      "scenic ~ [('scenic', 100), ('scirocco', 57), ('seicento', 57)]\n",
      "a4 ~ [('a4', 100), ('meriva', 60), ('corsa', 60)]\n",
      "a1 ~ [('a1', 100), ('meriva', 60), ('corsa', 60)]\n",
      "insignia ~ [('insignia', 100), ('niva', 77), ('a8', 60)]\n",
      "combo ~ [('combo', 100), ('croma', 60), ('doblo', 60)]\n",
      "focus ~ [('focus', 100), ('modus', 60), ('fox', 60)]\n",
      "tt ~ [('tt', 100), ('kadett', 90), ('jetta', 90)]\n",
      "a6 ~ [('a6', 100), ('meriva', 60), ('corsa', 60)]\n",
      "jazz ~ [('jazz', 100), ('ka', 45), ('a8', 45)]\n",
      "omega ~ [('omega', 100), ('megane', 73), ('one', 60)]\n",
      "slk ~ [('slk', 100), ('sl', 90), ('clk', 67)]\n",
      "7er ~ [('7er', 100), ('3er', 67), ('5er', 67)]\n",
      "80 ~ [('80', 100), ('850', 80), ('a8', 50)]\n",
      "147 ~ [('147', 100), ('145', 67), ('c4', 45)]\n",
      "100 ~ [('100', 100), ('500', 67), ('900', 67)]\n",
      "z_reihe ~ [('z_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]\n",
      "sportage ~ [('sportage', 100), ('range_rover_sport', 69), ('bora', 68)]\n",
      "sorento ~ [('sorento', 100), ('seicento', 67), ('fortwo', 62)]\n",
      "v40 ~ [('v40', 100), ('v50', 67), ('v70', 67)]\n",
      "ibiza ~ [('ibiza', 100), ('fabia', 60), ('a8', 60)]\n",
      "mustang ~ [('mustang', 100), ('musa', 73), ('gl', 60)]\n",
      "eos ~ [('eos', 100), ('mondeo', 72), ('terios', 67)]\n",
      "touran ~ [('touran', 100), ('bora', 68), ('tiguan', 67)]\n",
      "getz ~ [('getz', 100), ('sportage', 60), ('voyager', 51)]\n",
      "a3 ~ [('a3', 100), ('meriva', 60), ('corsa', 60)]\n",
      "almera ~ [('almera', 100), ('altea', 73), ('rav', 72)]\n",
      "megane ~ [('megane', 100), ('omega', 73), ('one', 60)]\n",
      "lupo ~ [('lupo', 100), ('up', 90), ('no_value', 51)]\n",
      "r19 ~ [('r19', 100), ('159', 67), ('90', 60)]\n",
      "zafira ~ [('zafira', 100), ('rav', 72), ('calibra', 62)]\n",
      "caddy ~ [('caddy', 100), ('cayenne', 50), ('charade', 50)]\n",
      "mondeo ~ [('mondeo', 100), ('eos', 72), ('one', 67)]\n",
      "cordoba ~ [('cordoba', 100), ('corolla', 71), ('corsa', 67)]\n",
      "colt ~ [('colt', 100), ('escort', 68), ('cl', 67)]\n",
      "impreza ~ [('impreza', 100), ('a8', 60), ('a4', 60)]\n",
      "vectra ~ [('vectra', 100), ('rav', 72), ('a8', 60)]\n",
      "berlingo ~ [('berlingo', 100), ('golf', 60), ('3er', 60)]\n",
      "tiguan ~ [('tiguan', 100), ('tigra', 73), ('touran', 67)]\n",
      "i_reihe ~ [('i_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]\n",
      "espace ~ [('espace', 100), ('eos', 60), ('ceed', 60)]\n",
      "sharan ~ [('sharan', 100), ('samara', 67), ('charade', 62)]\n",
      "6_reihe ~ [('6_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]\n",
      "panda ~ [('panda', 100), ('grand', 60), ('santa', 60)]\n",
      "up ~ [('up', 100), ('lupo', 90), ('superb', 90)]\n",
      "seicento ~ [('seicento', 100), ('sorento', 67), ('picanto', 67)]\n",
      "ceed ~ [('ceed', 100), ('espace', 60), ('cayenne', 55)]\n",
      "5_reihe ~ [('5_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]\n",
      "yeti ~ [('yeti', 100), ('i3', 60), ('x_type', 51)]\n",
      "octavia ~ [('octavia', 100), ('rav', 60), ('carisma', 57)]\n",
      "mii ~ [('mii', 100), ('civic', 60), ('micra', 60)]\n",
      "rx_reihe ~ [('rx_reihe', 100), ('x_reihe', 93), ('xc_reihe', 88)]\n",
      "6er ~ [('6er', 100), ('3er', 67), ('5er', 67)]\n",
      "modus ~ [('modus', 100), ('musa', 67), ('focus', 60)]\n",
      "fox ~ [('fox', 100), ('fortwo', 60), ('forester', 60)]\n",
      "matiz ~ [('matiz', 100), ('materia', 67), ('elefantino', 54)]\n",
      "beetle ~ [('beetle', 100), ('leon', 60), ('toledo', 50)]\n",
      "c1 ~ [('c1', 100), ('300c', 60), ('c4', 50)]\n",
      "rio ~ [('rio', 100), ('sirion', 90), ('terios', 90)]\n",
      "touareg ~ [('touareg', 100), ('touran', 62), ('gl', 60)]\n",
      "logan ~ [('logan', 100), ('leon', 67), ('grand', 60)]\n",
      "spider ~ [('spider', 100), ('sprinter', 71), ('superb', 67)]\n",
      "cuore ~ [('cuore', 100), ('corsa', 60), ('one', 60)]\n",
      "s_max ~ [('s_max', 100), ('c_max', 80), ('b_max', 80)]\n",
      "a2 ~ [('a2', 100), ('meriva', 60), ('corsa', 60)]\n",
      "galaxy ~ [('galaxy', 100), ('galant', 67), ('glk', 60)]\n",
      "c3 ~ [('c3', 100), ('300c', 60), ('c4', 50)]\n",
      "viano ~ [('viano', 100), ('vivaro', 73), ('vito', 67)]\n",
      "s_klasse ~ [('s_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]\n",
      "1_reihe ~ [('1_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]\n",
      "avensis ~ [('avensis', 100), ('aveo', 68), ('eos', 60)]\n",
      "roomster ~ [('roomster', 100), ('roadster', 75), ('boxster', 67)]\n",
      "sl ~ [('sl', 100), ('slk', 90), ('focus', 60)]\n",
      "kaefer ~ [('kaefer', 100), ('ka', 90), ('3er', 60)]\n",
      "santa ~ [('santa', 100), ('antara', 73), ('panda', 60)]\n",
      "cooper ~ [('cooper', 100), ('3er', 60), ('5er', 60)]\n",
      "leon ~ [('leon', 100), ('ypsilon', 77), ('phaeton', 68)]\n",
      "4_reihe ~ [('4_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]\n",
      "a5 ~ [('a5', 100), ('meriva', 60), ('corsa', 60)]\n",
      "500 ~ [('500', 100), ('100', 67), ('v50', 67)]\n",
      "laguna ~ [('laguna', 100), ('niva', 60), ('panda', 55)]\n",
      "ptcruiser ~ [('ptcruiser', 100), ('duster', 66), ('primera', 62)]\n",
      "clk ~ [('clk', 100), ('cl', 90), ('slk', 67)]\n",
      "primera ~ [('primera', 100), ('rav', 72), ('sprinter', 67)]\n",
      "x_reihe ~ [('x_reihe', 100), ('xc_reihe', 93), ('rx_reihe', 93)]\n",
      "exeo ~ [('exeo', 100), ('eos', 57), ('toledo', 51)]\n",
      "159 ~ [('159', 100), ('156', 67), ('r19', 67)]\n",
      "transit ~ [('transit', 100), ('transporter', 67), ('touran', 62)]\n",
      "juke ~ [('juke', 100), ('no_value', 51), ('range_rover_evoque', 51)]\n",
      "qashqai ~ [('qashqai', 100), ('i3', 60), ('arosa', 50)]\n",
      "carisma ~ [('carisma', 100), ('arosa', 67), ('yaris', 67)]\n",
      "accord ~ [('accord', 100), ('cc', 90), ('cordoba', 62)]\n",
      "corolla ~ [('corolla', 100), ('cordoba', 71), ('corsa', 67)]\n",
      "lanos ~ [('lanos', 100), ('arosa', 60), ('eos', 60)]\n",
      "phaeton ~ [('phaeton', 100), ('one', 72), ('leon', 68)]\n",
      "verso ~ [('verso', 100), ('range_rover_sport', 72), ('range_rover', 68)]\n",
      "swift ~ [('swift', 100), ('tt', 60), ('transit', 50)]\n",
      "rav ~ [('rav', 100), ('bravo', 90), ('navara', 72)]\n",
      "picanto ~ [('picanto', 100), ('punto', 67), ('seicento', 67)]\n",
      "boxster ~ [('boxster', 100), ('other', 67), ('forester', 67)]\n",
      "kalos ~ [('kalos', 100), ('ka', 90), ('arosa', 60)]\n",
      "superb ~ [('superb', 100), ('up', 90), ('spider', 67)]\n",
      "stilo ~ [('stilo', 100), ('ypsilon', 67), ('rio', 60)]\n",
      "alhambra ~ [('alhambra', 100), ('bora', 77), ('rav', 72)]\n",
      "mx_reihe ~ [('mx_reihe', 100), ('x_reihe', 93), ('m_reihe', 93)]\n",
      "roadster ~ [('roadster', 100), ('roomster', 75), ('duster', 71)]\n",
      "ypsilon ~ [('ypsilon', 100), ('leon', 77), ('one', 72)]\n",
      "cayenne ~ [('cayenne', 100), ('one', 60), ('ceed', 55)]\n",
      "galant ~ [('galant', 100), ('galaxy', 67), ('tt', 60)]\n",
      "justy ~ [('justy', 100), ('duster', 55), ('s_type', 55)]\n",
      "90 ~ [('90', 100), ('900', 90), ('9000', 90)]\n",
      "sirion ~ [('sirion', 100), ('rio', 90), ('one', 72)]\n",
      "crossfire ~ [('crossfire', 100), ('eos', 60), ('rio', 60)]\n",
      "agila ~ [('agila', 100), ('fabia', 60), ('altea', 60)]\n",
      "duster ~ [('duster', 100), ('roadster', 71), ('ptcruiser', 66)]\n",
      "cr_reihe ~ [('cr_reihe', 100), ('c_reihe', 93), ('xc_reihe', 88)]\n",
      "v50 ~ [('v50', 100), ('v40', 67), ('500', 67)]\n",
      "c_reihe ~ [('c_reihe', 100), ('xc_reihe', 93), ('cr_reihe', 93)]\n",
      "v_klasse ~ [('v_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]\n",
      "m_klasse ~ [('m_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]\n",
      "yaris ~ [('yaris', 100), ('auris', 80), ('carisma', 67)]\n",
      "c5 ~ [('c5', 100), ('300c', 60), ('c4', 50)]\n",
      "aygo ~ [('aygo', 100), ('kangoo', 68), ('sportage', 51)]\n",
      "cc ~ [('cc', 100), ('scirocco', 90), ('accord', 90)]\n",
      "carnival ~ [('carnival', 100), ('niva', 90), ('carisma', 67)]\n",
      "fusion ~ [('fusion', 100), ('one', 72), ('sirion', 67)]\n",
      "911 ~ [('911', 100), ('a1', 45), ('c1', 45)]\n",
      "bora ~ [('bora', 100), ('alhambra', 77), ('calibra', 77)]\n",
      "forfour ~ [('forfour', 100), ('fortwo', 62), ('fox', 60)]\n",
      "m_reihe ~ [('m_reihe', 100), ('mx_reihe', 93), ('2_reihe', 86)]\n",
      "cl ~ [('cl', 100), ('clio', 90), ('clk', 90)]\n",
      "tigra ~ [('tigra', 100), ('tiguan', 73), ('rav', 72)]\n",
      "300c ~ [('300c', 100), ('c4', 60), ('c1', 60)]\n",
      "spark ~ [('spark', 100), ('ka', 60), ('espace', 55)]\n",
      "v70 ~ [('v70', 100), ('v40', 67), ('v50', 67)]\n",
      "kuga ~ [('kuga', 100), ('ka', 67), ('a8', 60)]\n",
      "x_type ~ [('x_type', 100), ('s_type', 83), ('yeti', 51)]\n",
      "ducato ~ [('ducato', 100), ('picanto', 62), ('punto', 55)]\n",
      "s_type ~ [('s_type', 100), ('x_type', 83), ('justy', 55)]\n",
      "x_trail ~ [('x_trail', 100), ('rio', 60), ('rav', 60)]\n",
      "toledo ~ [('toledo', 100), ('leon', 68), ('polo', 60)]\n",
      "altea ~ [('altea', 100), ('almera', 73), ('materia', 67)]\n",
      "voyager ~ [('voyager', 100), ('3er', 60), ('5er', 60)]\n",
      "calibra ~ [('calibra', 100), ('bora', 77), ('rav', 72)]\n",
      "bravo ~ [('bravo', 100), ('rav', 90), ('alhambra', 68)]\n",
      "antara ~ [('antara', 100), ('astra', 73), ('santa', 73)]\n",
      "tucson ~ [('tucson', 100), ('one', 72), ('fusion', 67)]\n",
      "citigo ~ [('citigo', 100), ('golf', 60), ('clio', 60)]\n",
      "jimny ~ [('jimny', 100), ('i3', 45), ('viano', 40)]\n",
      "wrangler ~ [('wrangler', 100), ('gl', 90), ('range_rover_sport', 68)]\n",
      "lybra ~ [('lybra', 100), ('rav', 72), ('bora', 67)]\n",
      "q7 ~ [('q7', 100), ('q3', 50), ('q5', 50)]\n",
      "lancer ~ [('lancer', 100), ('freelander', 75), ('outlander', 75)]\n",
      "captiva ~ [('captiva', 100), ('niva', 68), ('carnival', 67)]\n",
      "c2 ~ [('c2', 100), ('300c', 60), ('c4', 50)]\n",
      "discovery ~ [('discovery', 100), ('move', 68), ('3er', 60)]\n",
      "freelander ~ [('freelander', 100), ('lancer', 75), ('defender', 67)]\n",
      "sandero ~ [('sandero', 100), ('rio', 72), ('mondeo', 62)]\n",
      "note ~ [('note', 100), ('transporter', 68), ('sprinter', 68)]\n",
      "900 ~ [('900', 100), ('90', 90), ('9000', 86)]\n",
      "cherokee ~ [('cherokee', 100), ('3er', 60), ('5er', 60)]\n",
      "clubman ~ [('clubman', 100), ('cl', 90), ('clk', 60)]\n",
      "samara ~ [('samara', 100), ('rav', 72), ('navara', 67)]\n",
      "defender ~ [('defender', 100), ('freelander', 67), ('3er', 60)]\n",
      "601 ~ [('601', 100), ('s60', 67), ('v60', 67)]\n",
      "cx_reihe ~ [('cx_reihe', 100), ('x_reihe', 93), ('c_reihe', 93)]\n",
      "legacy ~ [('legacy', 100), ('omega', 55), ('logan', 55)]\n",
      "pajero ~ [('pajero', 100), ('rio', 72), ('phaeton', 62)]\n",
      "auris ~ [('auris', 100), ('yaris', 80), ('carisma', 67)]\n",
      "niva ~ [('niva', 100), ('carnival', 90), ('insignia', 77)]\n",
      "s60 ~ [('s60', 100), ('601', 67), ('v60', 67)]\n",
      "nubira ~ [('nubira', 100), ('rav', 72), ('bora', 68)]\n",
      "vivaro ~ [('vivaro', 100), ('viano', 73), ('rio', 72)]\n",
      "g_klasse ~ [('g_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]\n",
      "lodgy ~ [('lodgy', 100), ('logan', 60), ('legacy', 55)]\n",
      "850 ~ [('850', 100), ('80', 80), ('500', 67)]\n",
      "range_rover ~ [('range_rover', 100), ('rangerover', 95), ('range_rover_sport', 90)]\n",
      "q3 ~ [('q3', 100), ('a3', 50), ('c3', 50)]\n",
      "serie_2 ~ [('serie_2', 100), ('serie_3', 86), ('serie_1', 86)]\n",
      "glk ~ [('glk', 100), ('gl', 90), ('slk', 67)]\n",
      "charade ~ [('charade', 100), ('sharan', 62), ('rav', 60)]\n",
      "croma ~ [('croma', 100), ('cordoba', 67), ('carisma', 67)]\n",
      "outlander ~ [('outlander', 100), ('lancer', 75), ('freelander', 63)]\n",
      "doblo ~ [('doblo', 100), ('polo', 67), ('combo', 60)]\n",
      "musa ~ [('musa', 100), ('mustang', 73), ('modus', 67)]\n",
      "move ~ [('move', 100), ('discovery', 68), ('range_rover', 68)]\n",
      "9000 ~ [('9000', 100), ('90', 90), ('900', 86)]\n",
      "v60 ~ [('v60', 100), ('v40', 67), ('v50', 67)]\n",
      "145 ~ [('145', 100), ('156', 67), ('147', 67)]\n",
      "aveo ~ [('aveo', 100), ('avensis', 68), ('phaeton', 68)]\n",
      "200 ~ [('200', 100), ('100', 67), ('500', 67)]\n",
      "b_max ~ [('b_max', 100), ('c_max', 80), ('s_max', 80)]\n",
      "range_rover_sport ~ [('range_rover_sport', 100), ('range_rover', 90), ('rangerover', 81)]\n",
      "terios ~ [('terios', 100), ('rio', 90), ('eos', 67)]\n",
      "rangerover ~ [('rangerover', 100), ('range_rover', 95), ('range_rover_sport', 81)]\n",
      "q5 ~ [('q5', 100), ('a5', 50), ('c5', 50)]\n",
      "range_rover_evoque ~ [('range_rover_evoque', 100), ('range_rover', 90), ('rangerover', 81)]\n",
      "materia ~ [('materia', 100), ('meriva', 77), ('astra', 67)]\n",
      "delta ~ [('delta', 100), ('a8', 60), ('jetta', 60)]\n",
      "gl ~ [('gl', 100), ('wrangler', 90), ('glk', 90)]\n",
      "kalina ~ [('kalina', 100), ('ka', 90), ('calibra', 62)]\n",
      "amarok ~ [('amarok', 100), ('samara', 67), ('ka', 60)]\n",
      "elefantino ~ [('elefantino', 100), ('twingo', 72), ('rio', 60)]\n",
      "i3 ~ [('i3', 100), ('yeti', 60), ('qashqai', 60)]\n",
      "kappa ~ [('kappa', 100), ('ka', 90), ('kalina', 55)]\n",
      "serie_3 ~ [('serie_3', 100), ('serie_2', 86), ('serie_1', 86)]\n",
      "serie_1 ~ [('serie_1', 100), ('serie_2', 86), ('serie_3', 86)]\n"
     ]
    }
   ],
   "source": [
    "# Анализ нефвных совпадений \n",
    "# признака \"Model\"\n",
    "for i in data['Model'].fillna('no_value').unique():\n",
    "    print(i, '~', process.extract(i, data['Model'].fillna('no_value').unique(), limit=3)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "volkswagen ~ [('volkswagen', 100), ('volvo', 54), ('opel', 45)]\n",
      "audi ~ [('audi', 100), ('hyundai', 60), ('subaru', 51)]\n",
      "jeep ~ [('jeep', 100), ('peugeot', 36), ('chevrolet', 31)]\n",
      "skoda ~ [('skoda', 100), ('honda', 60), ('kia', 50)]\n",
      "bmw ~ [('bmw', 100), ('volkswagen', 30), ('mazda', 30)]\n",
      "peugeot ~ [('peugeot', 100), ('opel', 45), ('seat', 45)]\n",
      "ford ~ [('ford', 100), ('mercedes_benz', 45), ('alfa_romeo', 45)]\n",
      "mazda ~ [('mazda', 100), ('lada', 67), ('audi', 44)]\n",
      "nissan ~ [('nissan', 100), ('saab', 51), ('seat', 45)]\n",
      "renault ~ [('renault', 100), ('seat', 55), ('sonstige_autos', 51)]\n",
      "mercedes_benz ~ [('mercedes_benz', 100), ('ford', 45), ('seat', 45)]\n",
      "opel ~ [('opel', 100), ('citroen', 51), ('volkswagen', 45)]\n",
      "seat ~ [('seat', 100), ('smart', 67), ('renault', 55)]\n",
      "citroen ~ [('citroen', 100), ('opel', 51), ('chevrolet', 50)]\n",
      "honda ~ [('honda', 100), ('hyundai', 67), ('skoda', 60)]\n",
      "fiat ~ [('fiat', 100), ('daihatsu', 68), ('kia', 57)]\n",
      "mini ~ [('mini', 100), ('nissan', 45), ('mitsubishi', 45)]\n",
      "smart ~ [('smart', 100), ('seat', 67), ('subaru', 55)]\n",
      "hyundai ~ [('hyundai', 100), ('honda', 67), ('audi', 60)]\n",
      "sonstige_autos ~ [('sonstige_autos', 100), ('renault', 51), ('audi', 45)]\n",
      "alfa_romeo ~ [('alfa_romeo', 100), ('rover', 54), ('land_rover', 50)]\n",
      "subaru ~ [('subaru', 100), ('smart', 55), ('audi', 51)]\n",
      "volvo ~ [('volvo', 100), ('volkswagen', 54), ('chevrolet', 54)]\n",
      "mitsubishi ~ [('mitsubishi', 100), ('audi', 45), ('fiat', 45)]\n",
      "kia ~ [('kia', 100), ('suzuki', 72), ('lancia', 60)]\n",
      "suzuki ~ [('suzuki', 100), ('kia', 72), ('subaru', 50)]\n",
      "lancia ~ [('lancia', 100), ('dacia', 73), ('kia', 60)]\n",
      "toyota ~ [('toyota', 100), ('sonstige_autos', 40), ('audi', 36)]\n",
      "chevrolet ~ [('chevrolet', 100), ('chrysler', 59), ('volvo', 54)]\n",
      "dacia ~ [('dacia', 100), ('lancia', 73), ('daihatsu', 72)]\n",
      "daihatsu ~ [('daihatsu', 100), ('dacia', 72), ('fiat', 68)]\n",
      "trabant ~ [('trabant', 100), ('seat', 45), ('fiat', 45)]\n",
      "saab ~ [('saab', 100), ('nissan', 51), ('seat', 50)]\n",
      "chrysler ~ [('chrysler', 100), ('chevrolet', 59), ('rover', 46)]\n",
      "jaguar ~ [('jaguar', 100), ('subaru', 50), ('audi', 45)]\n",
      "daewoo ~ [('daewoo', 100), ('lada', 45), ('alfa_romeo', 38)]\n",
      "porsche ~ [('porsche', 100), ('ford', 45), ('seat', 45)]\n",
      "rover ~ [('rover', 100), ('land_rover', 90), ('alfa_romeo', 54)]\n",
      "land_rover ~ [('land_rover', 100), ('rover', 90), ('lada', 68)]\n",
      "lada ~ [('lada', 100), ('land_rover', 68), ('mazda', 67)]\n"
     ]
    }
   ],
   "source": [
    "# Анализ нефвных совпадений \n",
    "# признака \"Brand\"\n",
    "for i in data['Brand'].unique():\n",
    "    print(i, '~', process.extract(i, data['Brand'].unique(), limit=3)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>RegistrationYear</th>\n",
       "      <th>Power</th>\n",
       "      <th>Kilometer</th>\n",
       "      <th>RegistrationMonth</th>\n",
       "      <th>NumberOfPictures</th>\n",
       "      <th>PostalCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026916</td>\n",
       "      <td>0.158872</td>\n",
       "      <td>-0.333199</td>\n",
       "      <td>0.110581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.076055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RegistrationYear</th>\n",
       "      <td>0.026916</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000828</td>\n",
       "      <td>-0.053447</td>\n",
       "      <td>-0.011619</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.003459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Power</th>\n",
       "      <td>0.158872</td>\n",
       "      <td>-0.000828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024002</td>\n",
       "      <td>0.043380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.021665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kilometer</th>\n",
       "      <td>-0.333199</td>\n",
       "      <td>-0.053447</td>\n",
       "      <td>0.024002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RegistrationMonth</th>\n",
       "      <td>0.110581</td>\n",
       "      <td>-0.011619</td>\n",
       "      <td>0.043380</td>\n",
       "      <td>0.009571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.013995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfPictures</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostalCode</th>\n",
       "      <td>0.076055</td>\n",
       "      <td>-0.003459</td>\n",
       "      <td>0.021665</td>\n",
       "      <td>-0.007698</td>\n",
       "      <td>0.013995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price  RegistrationYear     Power  Kilometer  \\\n",
       "Price              1.000000          0.026916  0.158872  -0.333199   \n",
       "RegistrationYear   0.026916          1.000000 -0.000828  -0.053447   \n",
       "Power              0.158872         -0.000828  1.000000   0.024002   \n",
       "Kilometer         -0.333199         -0.053447  0.024002   1.000000   \n",
       "RegistrationMonth  0.110581         -0.011619  0.043380   0.009571   \n",
       "NumberOfPictures        NaN               NaN       NaN        NaN   \n",
       "PostalCode         0.076055         -0.003459  0.021665  -0.007698   \n",
       "\n",
       "                   RegistrationMonth  NumberOfPictures  PostalCode  \n",
       "Price                       0.110581               NaN    0.076055  \n",
       "RegistrationYear           -0.011619               NaN   -0.003459  \n",
       "Power                       0.043380               NaN    0.021665  \n",
       "Kilometer                   0.009571               NaN   -0.007698  \n",
       "RegistrationMonth           1.000000               NaN    0.013995  \n",
       "NumberOfPictures                 NaN               NaN         NaN  \n",
       "PostalCode                  0.013995               NaN    1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка коррелируемости\n",
    "# признаков датафрейма\n",
    "data.corr() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Выводы из анализа данных**\n",
    "\n",
    "1. Датафрейм содержит 354369 объектов и 16 признаков, 7 из которых являются 64-битными целочисленными, а 9 типа `object`. Целевым является целочисленный признак `Price`. \n",
    "1. В данных есть пропуски. Их количество в разных признаках более 1% от всего количества. Поэтому, их нельзя удалить, а требуется заменить на значение `no_value`. \n",
    "1. Категориальные признаки `VehicleType`, `FuelType` и `Model` имеют значения `other`, которые можно изменить на `no_value` также, как и пропуски.\n",
    "1. По мнению автора данной работы, следующие признаки могут быть неинформативными для моделей машинного обучения:\n",
    " 1. `DateCrawled` — дата скачивания анкеты из базы (может влиять на цену относительно даты размещения объявления, но незначительно)\n",
    " 1. `RegistrationMonth` — месяц регистрации автомобиля (большее значение имеет год регистрации)\n",
    " 1. `DateCreated` — дата создания анкеты (в целях предсказания цен автомобилей в будущих анкетах эта информация не акутальна)\n",
    " 1. `NumberOfPictures` — количество фотографий автомобиля (может влиять на цену, т.к. фотографии продоваемого объекта вызывают доверие, но признак содержит только нули)\n",
    " 1. `PostalCode` — почтовый индекс владельца анкеты (может влиять, если местоположение продавца и покупателя имеет значение, но это не точно)\n",
    " 1. `LastSeen` — дата последней активности пользователя (может указывать на продолжительность наличия объявления в сети, но этот признак в меньшей степени может влиять на цену, чем другие признаки, описывающие характеристики продаваемого авто)\n",
    "1. Числовые данные не распределены нормально и имеют выбросы. \n",
    "1. Числовые признаки имеют разный диапазон. Для использования в машинном обучении их требуется стандартизировать. \n",
    "1. Корреляция аттрибутов между собой и с целевым признаком слабая. Наибольшей обратной корреляцией с целевым признаком обладают признаки `RegistrationYear`.\n",
    "1. Анализ текстовых категриальных признаков `Model` и `Brand` выявил неявное совпадение значений `range_rover` и `rangerover` в признаке `Model`. Эти значения следует объеденить в `range_rover`.\n",
    "1. Названия признаков не в \"змеином\" стиле. Можно привести их в соответствие со \"змеиный\" стилем.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные с пропусками:\n",
    "1. `VehicleType` — тип автомобильного кузова\n",
    "1. `Gearbox` — тип коробки передач\n",
    "1. `Model` — модель автомобиля\n",
    "1. `FuelType` — тип топлива\n",
    "1. `Repaired` — была машина в ремонте или нет\n",
    "\n",
    "Все признаки с пропусками категориального типа `object`. Для них будет использована категория `no_value`, указывающая на отсутствие значений."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ненормальности в данных:\n",
    "1. Атрибут `RegistrationYear` содержит 171 значение с годами производства автомобилей меньше 1900 года и больше 2023 года. Объектами с этими значениями можно принебречь, т.к. их мне 1% от всего количества объектов. Их требуется удалить. \n",
    "1. Атрибут `Power` содержит более 11% значений с мощностью двигателя выходящую за пределы известных значейни. Например, меньше, чем у самого маломощного автомобиля \"Benz Patent Motorwagen\", у которого мощность двигателя равна 0.75 л.с.  (*https://1gai-ru.turbopages.org/turbo/1gai.ru/s/blog/cars/513900-desyat-samyh-malomoschnyh-avtomobiley.html*, 2023). Также атрибут `Power`содержит значения мощности двигателя более 5000 л.с.. Это превышает мощность самого мощного автомобиля Devel Sixteen (*https://www.driver-helper.ru/text/sovetiy/top-10-samyx-moshhnyx-serijnyx-avto-v-mire*). Учитывая большую долю подобных объектов и тот факт, что подобные объекты могут появиться в эксплуатационном данных, вместо того, чтобы от них избавляться, в них следует заменить ненормальные значения `Power` на медианные для каждой группы связки `Brand` `Model`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Предобработка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Замена пропусков \n",
    "# на значение \"no_value\" \n",
    "for i in data.columns:\n",
    "    if data_shape - data[i].loc[data[i].notna()].shape[0] > 0:\n",
    "        data.loc[data[i].isna(), i] = 'no_value'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Замена значения \"other\" на \"no_value\" \n",
    "# для унификации отсутствующей информации \n",
    "# в признаках \"VehicleType\" и \"FuellType\"\n",
    "data.loc[\n",
    "    (data['VehicleType'] == 'other') | \n",
    "    (data['FuelType'] == 'other') |\n",
    "    (data['Model'] == 'other'), \n",
    "    ['VehicleType', 'FuelType', 'Model'] \n",
    "] = 'no_value' \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-04-07 14:36:58')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Определение максимальной даты просмотра объявления \n",
    "# для установки в качестве предельного срока \n",
    "# выпуска автомобиля\n",
    "data['DateCrawled'] = pd.to_datetime(\n",
    "    data['DateCrawled'], format='%Y-%m-%dT%H:%M:%S'\n",
    ")\n",
    "date_crawled_max = data['DateCrawled'].max()\n",
    "date_crawled_max\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Удаление неинформативных признаков\n",
    "data = data.drop([\n",
    "    'DateCrawled', \n",
    "    'RegistrationMonth', \n",
    "    'DateCreated', \n",
    "    'NumberOfPictures', \n",
    "    'PostalCode', \n",
    "    'LastSeen'\n",
    "], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Удаление ненормальностей в данных\n",
    "# признака \"RegistrationYear\"\n",
    "data = data.loc[\n",
    "    (data['RegistrationYear'] > 1900) & \n",
    "    (data['RegistrationYear'] < date_crawled_max.year)\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           0.0\n",
       "1         190.0\n",
       "2         163.0\n",
       "3          75.0\n",
       "4          69.0\n",
       "          ...  \n",
       "354364      0.0\n",
       "354365      0.0\n",
       "354366    101.0\n",
       "354367    102.0\n",
       "354368    100.0\n",
       "Name: Power, Length: 330174, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Замена ненормальностей в данных \n",
    "# признака \"Power\" в более 10% объектов\n",
    "\n",
    "#power_group_median = data.groupby(['Brand', 'Model'])['Power'].median()\n",
    "power_group_median = data.pivot_table(values='Power', index=['Brand', 'Model'], aggfunc='median')\n",
    "\n",
    "for i in power_group_median.index:\n",
    "    data['Power'] = np.where(\n",
    "        ((data['Power'] < .75) | (data['Power'] > 5000)) & \n",
    "        ((data['Power'] == i[0]) & (data['Model'] == i[1])), \n",
    "        power_group_median.loc[i], \n",
    "        data['Power']\n",
    "    )\n",
    "\n",
    "data['Power']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Объединение неявно совпадающих значений признака \"Model\" \n",
    "# \"range_rover\" и \"rangerover\" в \"range_rover\"\n",
    "data.loc[data['Model'] == 'rangerover', 'Model'] = 'range_rover'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Приведение названий признаков датафрейма\n",
    "# к \"змеиному\" стилю\n",
    "data.columns = [re.sub(r'(?<!^)(?=[A-Z])', '_', i).lower() for i in data.columns] \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Проверка результатов предобработкаи данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 330174 entries, 0 to 354368\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   price              330174 non-null  int64  \n",
      " 1   vehicle_type       330174 non-null  object \n",
      " 2   registration_year  330174 non-null  int64  \n",
      " 3   gearbox            330174 non-null  object \n",
      " 4   power              330174 non-null  float64\n",
      " 5   model              330174 non-null  object \n",
      " 6   kilometer          330174 non-null  int64  \n",
      " 7   fuel_type          330174 non-null  object \n",
      " 8   brand              330174 non-null  object \n",
      " 9   repaired           330174 non-null  object \n",
      "dtypes: float64(1), int64(3), object(6)\n",
      "memory usage: 27.7+ MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>vehicle_type</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>repaired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>480</td>\n",
       "      <td>no_value</td>\n",
       "      <td>1993</td>\n",
       "      <td>manual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no_value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manual</td>\n",
       "      <td>190.0</td>\n",
       "      <td>no_value</td>\n",
       "      <td>125000</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>audi</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9800</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>auto</td>\n",
       "      <td>163.0</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>jeep</td>\n",
       "      <td>no_value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500</td>\n",
       "      <td>small</td>\n",
       "      <td>2001</td>\n",
       "      <td>manual</td>\n",
       "      <td>75.0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3600</td>\n",
       "      <td>small</td>\n",
       "      <td>2008</td>\n",
       "      <td>manual</td>\n",
       "      <td>69.0</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>skoda</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>650</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1995</td>\n",
       "      <td>manual</td>\n",
       "      <td>102.0</td>\n",
       "      <td>3er</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>bmw</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2200</td>\n",
       "      <td>convertible</td>\n",
       "      <td>2004</td>\n",
       "      <td>manual</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2_reihe</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>peugeot</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>no_value</td>\n",
       "      <td>1980</td>\n",
       "      <td>manual</td>\n",
       "      <td>50.0</td>\n",
       "      <td>no_value</td>\n",
       "      <td>40000</td>\n",
       "      <td>no_value</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14500</td>\n",
       "      <td>bus</td>\n",
       "      <td>2014</td>\n",
       "      <td>manual</td>\n",
       "      <td>125.0</td>\n",
       "      <td>c_max</td>\n",
       "      <td>30000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>ford</td>\n",
       "      <td>no_value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>999</td>\n",
       "      <td>small</td>\n",
       "      <td>1998</td>\n",
       "      <td>manual</td>\n",
       "      <td>101.0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>no_value</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no_value</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price vehicle_type  registration_year gearbox  power     model  kilometer  \\\n",
       "0    480     no_value               1993  manual    0.0      golf     150000   \n",
       "1  18300        coupe               2011  manual  190.0  no_value     125000   \n",
       "2   9800          suv               2004    auto  163.0     grand     125000   \n",
       "3   1500        small               2001  manual   75.0      golf     150000   \n",
       "4   3600        small               2008  manual   69.0     fabia      90000   \n",
       "5    650        sedan               1995  manual  102.0       3er     150000   \n",
       "6   2200  convertible               2004  manual  109.0   2_reihe     150000   \n",
       "7      0     no_value               1980  manual   50.0  no_value      40000   \n",
       "8  14500          bus               2014  manual  125.0     c_max      30000   \n",
       "9    999        small               1998  manual  101.0      golf     150000   \n",
       "\n",
       "  fuel_type       brand  repaired  \n",
       "0    petrol  volkswagen  no_value  \n",
       "1  gasoline        audi       yes  \n",
       "2  gasoline        jeep  no_value  \n",
       "3    petrol  volkswagen        no  \n",
       "4  gasoline       skoda        no  \n",
       "5    petrol         bmw       yes  \n",
       "6    petrol     peugeot        no  \n",
       "7  no_value  volkswagen        no  \n",
       "8    petrol        ford  no_value  \n",
       "9  no_value  volkswagen  no_value  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка изменений\n",
    "data.info() \n",
    "data.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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   "source": [
    "# Проверка изменений\n",
    "data.hist() \n",
    "plt.subplots_adjust(wspace=.4, hspace=.5)\n",
    "\n",
    "data.describe() \n"
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  },
  {
   "cell_type": "code",
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      "Уникальные значения признака \"vehicle_type\": ['no_value' 'coupe' 'suv' 'small' 'sedan' 'convertible' 'bus' 'wagon']\n",
      "Всего унимальных значений признака \"vehicle_type\": 8\n",
      "\n",
      "Уникальные значения признака \"gearbox\": ['manual' 'auto' 'no_value']\n",
      "Всего унимальных значений признака \"gearbox\": 3\n",
      "\n",
      "Уникальные значения признака \"model\": ['golf' 'no_value' 'grand' 'fabia' '3er' '2_reihe' 'c_max' '3_reihe'\n",
      " 'passat' 'navara' 'ka' 'twingo' 'a_klasse' 'scirocco' '5er' 'arosa'\n",
      " 'civic' 'transporter' 'punto' 'e_klasse' 'corsa' 'one' 'fortwo' 'clio'\n",
      " '1er' 'b_klasse' 'signum' 'astra' 'a8' 'jetta' 'polo' 'fiesta' 'c_klasse'\n",
      " 'micra' 'sprinter' '156' 'escort' 'forester' 'xc_reihe' 'scenic' 'a4'\n",
      " 'a1' 'insignia' 'combo' 'focus' 'tt' 'a6' 'jazz' 'omega' 'slk' '7er' '80'\n",
      " '147' '100' 'meriva' 'z_reihe' 'sorento' 'v40' 'ibiza' 'mustang' 'eos'\n",
      " 'vito' 'touran' 'getz' 'a3' 'megane' 'lupo' 'r19' 'caddy' 'mondeo'\n",
      " 'cordoba' 'colt' 'impreza' 'vectra' 'berlingo' 'tiguan' 'sharan'\n",
      " '6_reihe' 'c4' 'panda' 'up' 'i_reihe' 'ceed' 'kangoo' '5_reihe' 'yeti'\n",
      " 'octavia' 'zafira' 'mii' 'rx_reihe' '6er' 'fox' 'matiz' 'beetle' 'rio'\n",
      " 'touareg' 'logan' 'spider' 'cuore' 's_max' 'modus' 'a2' 'galaxy' 'c3'\n",
      " 'viano' 's_klasse' '1_reihe' 'avensis' 'roomster' 'sl' 'kaefer' 'santa'\n",
      " 'cooper' 'leon' '4_reihe' 'a5' 'sportage' 'laguna' 'ptcruiser' 'clk'\n",
      " 'primera' 'espace' 'x_reihe' 'exeo' '159' 'transit' 'juke' 'qashqai'\n",
      " 'carisma' 'accord' 'corolla' 'lanos' 'phaeton' 'verso' 'swift' 'rav'\n",
      " 'picanto' 'boxster' 'kalos' 'superb' 'stilo' 'alhambra' 'mx_reihe'\n",
      " 'roadster' 'ypsilon' 'cayenne' 'galant' 'justy' '90' 'sirion' 'crossfire'\n",
      " 'agila' 'duster' 'v50' '500' 'c_reihe' 'v_klasse' 'm_klasse' 'yaris' 'c5'\n",
      " 'aygo' 'almera' 'seicento' 'cc' 'fusion' '911' 'bora' 'forfour' 'm_reihe'\n",
      " 'cl' 'tigra' '300c' 'cr_reihe' 'spark' 'v70' 'kuga' 'x_type' 'ducato'\n",
      " 's_type' 'x_trail' 'toledo' 'altea' 'voyager' 'calibra' 'carnival'\n",
      " 'bravo' 'antara' 'tucson' 'c1' 'kadett' 'citigo' 'jimny' 'wrangler'\n",
      " 'lybra' 'q7' 'lancer' 'captiva' 'discovery' 'freelander' 'sandero' 'note'\n",
      " '900' 'cherokee' 'clubman' 'samara' 'defender' 'cx_reihe' 'legacy' '601'\n",
      " 'pajero' 'c2' 'niva' 's60' 'nubira' 'vivaro' 'g_klasse' 'auris' 'lodgy'\n",
      " '850' 'range_rover' 'q3' 'glk' 'charade' 'croma' 'outlander' 'doblo'\n",
      " 'musa' 'move' '9000' 'v60' '145' '200' 'b_max' 'range_rover_sport' 'aveo'\n",
      " 'terios' 'q5' 'range_rover_evoque' 'materia' 'delta' 'gl' 'serie_2'\n",
      " 'kalina' 'elefantino' 'i3' 'amarok' 'kappa' 'serie_3' 'serie_1']\n",
      "Всего унимальных значений признака \"model\": 249\n",
      "\n",
      "Уникальные значения признака \"fuel_type\": ['petrol' 'gasoline' 'no_value' 'lpg' 'cng' 'electric' 'hybrid']\n",
      "Всего унимальных значений признака \"fuel_type\": 7\n",
      "\n",
      "Уникальные значения признака \"brand\": ['volkswagen' 'audi' 'jeep' 'skoda' 'bmw' 'peugeot' 'ford' 'mazda'\n",
      " 'nissan' 'renault' 'mercedes_benz' 'seat' 'honda' 'fiat' 'opel' 'mini'\n",
      " 'smart' 'sonstige_autos' 'alfa_romeo' 'subaru' 'volvo' 'mitsubishi' 'kia'\n",
      " 'hyundai' 'suzuki' 'lancia' 'citroen' 'toyota' 'chevrolet' 'dacia'\n",
      " 'daihatsu' 'trabant' 'saab' 'chrysler' 'jaguar' 'daewoo' 'porsche'\n",
      " 'rover' 'land_rover' 'lada']\n",
      "Всего унимальных значений признака \"brand\": 40\n",
      "\n",
      "Уникальные значения признака \"repaired\": ['no_value' 'yes' 'no']\n",
      "Всего унимальных значений признака \"repaired\": 3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Анализ уникальных значений \n",
    "# категориальных текстовых признаков\n",
    "for i in data.select_dtypes(include='object').columns: \n",
    "    print(f'Уникальные значения признака \"{i}\":', data[i].unique()) \n",
    "    print(f'Всего унимальных значений признака \"{i}\":', len(data[i].unique())) \n",
    "    print() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Всего удалённо объектов: 24195\n",
      "Доля удалённых объектов: 6.827628827578036%\n"
     ]
    }
   ],
   "source": [
    "# Анализ количества \n",
    "# удалённых объектов\n",
    "print('Всего удалённо объектов:', data_shape - data.shape[0]) \n",
    "print(f'Доля удалённых объектов: {(1 - data.shape[0] / data_shape)*100}%') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>power</th>\n",
       "      <th>kilometer</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.490673</td>\n",
       "      <td>0.164822</td>\n",
       "      <td>-0.336981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registration_year</th>\n",
       "      <td>0.490673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.067158</td>\n",
       "      <td>-0.220855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power</th>\n",
       "      <td>0.164822</td>\n",
       "      <td>0.067158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kilometer</th>\n",
       "      <td>-0.336981</td>\n",
       "      <td>-0.220855</td>\n",
       "      <td>0.027308</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      price  registration_year     power  kilometer\n",
       "price              1.000000           0.490673  0.164822  -0.336981\n",
       "registration_year  0.490673           1.000000  0.067158  -0.220855\n",
       "power              0.164822           0.067158  1.000000   0.027308\n",
       "kilometer         -0.336981          -0.220855  0.027308   1.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Анализ коррляции признаков \n",
    "# после предобработки данных\n",
    "data.corr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval columns not set, guessing: ['price', 'registration_year', 'power', 'kilometer']\n"
     ]
    },
    {
     "data": {
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       "      <th>gearbox</th>\n",
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       "      <th>fuel_type</th>\n",
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       "      <th>repaired</th>\n",
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       "      <td>0.305974</td>\n",
       "      <td>0.005928</td>\n",
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       "      <td>0.311190</td>\n",
       "      <td>0.263280</td>\n",
       "      <td>0.356071</td>\n",
       "      <td>0.366852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.209775</td>\n",
       "      <td>0.336422</td>\n",
       "      <td>0.005379</td>\n",
       "      <td>0.942273</td>\n",
       "      <td>0.164271</td>\n",
       "      <td>0.573069</td>\n",
       "      <td>0.644883</td>\n",
       "      <td>0.207384</td>\n",
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       "    <tr>\n",
       "      <th>registration_year</th>\n",
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       "      <td>0.209775</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.146034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.574288</td>\n",
       "      <td>0.307834</td>\n",
       "      <td>0.274391</td>\n",
       "      <td>0.356596</td>\n",
       "      <td>0.235890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gearbox</th>\n",
       "      <td>0.305974</td>\n",
       "      <td>0.336422</td>\n",
       "      <td>0.146034</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008489</td>\n",
       "      <td>0.626205</td>\n",
       "      <td>0.070258</td>\n",
       "      <td>0.282519</td>\n",
       "      <td>0.523394</td>\n",
       "      <td>0.482828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power</th>\n",
       "      <td>0.005928</td>\n",
       "      <td>0.005379</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002184</td>\n",
       "      <td>0.013631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>0.567335</td>\n",
       "      <td>0.942273</td>\n",
       "      <td>0.574288</td>\n",
       "      <td>0.626205</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.437192</td>\n",
       "      <td>0.711109</td>\n",
       "      <td>0.997654</td>\n",
       "      <td>0.281206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kilometer</th>\n",
       "      <td>0.311190</td>\n",
       "      <td>0.164271</td>\n",
       "      <td>0.307834</td>\n",
       "      <td>0.070258</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.137378</td>\n",
       "      <td>0.276410</td>\n",
       "      <td>0.226748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel_type</th>\n",
       "      <td>0.263280</td>\n",
       "      <td>0.573069</td>\n",
       "      <td>0.274391</td>\n",
       "      <td>0.282519</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.355297</td>\n",
       "      <td>0.194107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand</th>\n",
       "      <td>0.356071</td>\n",
       "      <td>0.644883</td>\n",
       "      <td>0.356596</td>\n",
       "      <td>0.523394</td>\n",
       "      <td>0.002184</td>\n",
       "      <td>0.997654</td>\n",
       "      <td>0.276410</td>\n",
       "      <td>0.355297</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.164810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repaired</th>\n",
       "      <td>0.366852</td>\n",
       "      <td>0.207384</td>\n",
       "      <td>0.235890</td>\n",
       "      <td>0.482828</td>\n",
       "      <td>0.013631</td>\n",
       "      <td>0.281206</td>\n",
       "      <td>0.226748</td>\n",
       "      <td>0.194107</td>\n",
       "      <td>0.164810</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      price  vehicle_type  registration_year   gearbox  \\\n",
       "price              1.000000      0.276073           0.609296  0.305974   \n",
       "vehicle_type       0.276073      1.000000           0.209775  0.336422   \n",
       "registration_year  0.609296      0.209775           1.000000  0.146034   \n",
       "gearbox            0.305974      0.336422           0.146034  1.000000   \n",
       "power              0.005928      0.005379           0.000000  0.008489   \n",
       "model              0.567335      0.942273           0.574288  0.626205   \n",
       "kilometer          0.311190      0.164271           0.307834  0.070258   \n",
       "fuel_type          0.263280      0.573069           0.274391  0.282519   \n",
       "brand              0.356071      0.644883           0.356596  0.523394   \n",
       "repaired           0.366852      0.207384           0.235890  0.482828   \n",
       "\n",
       "                      power     model  kilometer  fuel_type     brand  \\\n",
       "price              0.005928  0.567335   0.311190   0.263280  0.356071   \n",
       "vehicle_type       0.005379  0.942273   0.164271   0.573069  0.644883   \n",
       "registration_year  0.000000  0.574288   0.307834   0.274391  0.356596   \n",
       "gearbox            0.008489  0.626205   0.070258   0.282519  0.523394   \n",
       "power              1.000000  0.000000   0.000000   0.000000  0.002184   \n",
       "model              0.000000  1.000000   0.437192   0.711109  0.997654   \n",
       "kilometer          0.000000  0.437192   1.000000   0.137378  0.276410   \n",
       "fuel_type          0.000000  0.711109   0.137378   1.000000  0.355297   \n",
       "brand              0.002184  0.997654   0.276410   0.355297  1.000000   \n",
       "repaired           0.013631  0.281206   0.226748   0.194107  0.164810   \n",
       "\n",
       "                   repaired  \n",
       "price              0.366852  \n",
       "vehicle_type       0.207384  \n",
       "registration_year  0.235890  \n",
       "gearbox            0.482828  \n",
       "power              0.013631  \n",
       "model              0.281206  \n",
       "kilometer          0.226748  \n",
       "fuel_type          0.194107  \n",
       "brand              0.164810  \n",
       "repaired           1.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Анализ коррляции признаков \n",
    "# после предобработки данных\n",
    "data.phik_matrix()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Выводы из преварительной добработки данных**\n",
    "\n",
    "Датафрейм был успешно оптимизирован и подготовлен к использованию в машинном обучении:\n",
    "1. Все неопределенные значения во всех признаках заменены на `no_value`.\n",
    "1. Удалены неинформативные признаки `DateCrawled`, `RegistrationMonth`, `DateCreated`, `NumberOfPictures`, `PostalCode`, `LastSeen`.\n",
    "1. Удален 171 объект с датой регистрации автомобиля в признаке `RegistrationYear` меньше 1900 года и старше 2023 года.\n",
    "1. Заменены значения признака `Power` с мощностью двигателя менее 0.75 л.с. и более 5000 на медианные значения групп `Brand` + `Model`.\n",
    "1. Изменены неявно совпадающие значения `range_rover` и `rangerover` признака `Model` на значение `range_rover`. \n",
    "1. Названия признаков приведены к \"змеиному\" стилю.\n",
    "\n",
    "После предвартиельной обработки данных все числовые признаки имеют среднюю и слабую корреляцию между собой и с целевым признаком. Наибольшей прямой корреляцией с целевым признаком обладает признак `registration_year`, а обратной корреляцией `kilometer`. После предварительной обработки данных стало очевидным то, что требуется все категориальные признаки кодировать с помощью технологии One Hot Encoding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обучение моделей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Полезные функции подготовки данных и подбора моделей и их параметров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функция для кодирования категориальных текстовых признаков\n",
    "# с помощью технологии One Hot Encoding (pd.get_dummies())\n",
    "def features_get_dummies(features, column_name):\n",
    "    features = features.join(\n",
    "        pd.get_dummies(\n",
    "            data[column_name], \n",
    "            prefix=column_name, \n",
    "            prefix_sep='_', \n",
    "            drop_first=True\n",
    "        )\n",
    "    )\n",
    "    features = features.drop(column_name, axis=1)\n",
    "    return features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функция подготовки данных перед подбором моделей и их параметров\n",
    "def data_preprocessing(data, target_name):\n",
    "    \n",
    "    # Перемешивание объектов \n",
    "    # для их лучшего распределения в выборках\n",
    "    data = shuffle(data, random_state=STATE)\n",
    "    \n",
    "    # Разделение датафреймов на целевую и нецелевую выборку\n",
    "    features = data.drop([target_name], axis=1)\n",
    "    target = data[target_name]\n",
    "\n",
    "    # Разделение целевой и нецелевой выборки \n",
    "    # на обучающие и тестовые выборки\n",
    "    features_train,  features_test, target_train, target_test = train_test_split(\n",
    "        features, \n",
    "        target, \n",
    "        test_size=.25, \n",
    "        random_state=STATE\n",
    "    )\n",
    "    \n",
    "    return features_train,  features_test, target_train, target_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функция создания структуры пайплайна\n",
    "def params_and_model_selection(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    model_params\n",
    "):\n",
    "    \n",
    "    start_time = time.time()\n",
    "    #funtion_time = %timeit\n",
    "    \n",
    "    # Стандартизация числовых значений\n",
    "    numeric_transformer = make_pipeline(\n",
    "        StandardScaler()\n",
    "    )\n",
    "    \n",
    "    # Шаг препроцессинга в Пайплайне\n",
    "    preprocessor = make_column_transformer(\n",
    "        (numeric_transformer, features_train.columns)\n",
    "    )\n",
    "    \n",
    "    # Pipeline\n",
    "    pipe = Pipeline([\n",
    "        ('preprocessor', preprocessor), \n",
    "        ('regressor', model_params[0]['regressor'][0])\n",
    "    ])\n",
    "    pipe.fit(features_train, target_train)\n",
    "    #predict = pipe.predict(features_test) # спорно, т.к. это должно быть в результате, а здесь должна использоваться валидационная выборка\n",
    "    \n",
    "    # Передача функции ошибки через make_scorer в HalvingGridSearchCV\n",
    "    smape_score = make_scorer(\n",
    "        mean_squared_error, \n",
    "        squared=False # Для RMSE\n",
    "    )\n",
    "    \n",
    "    # HalvingGridSearchCV\n",
    "    # (о подборе оптимальных параметров:\n",
    "    # https://scikit-learn.ru/3-2-tuning-the-hyper-parameters-of-an-estimator/)\n",
    "    #grid = HalvingRandomSearchCV(\n",
    "    grid = HalvingGridSearchCV(\n",
    "        pipe, \n",
    "        model_params, \n",
    "        cv=4, # параметр KFold для кроссвалидации (обучющая и валидационная выборки 75:25)\n",
    "        n_jobs=-1, # количество параллельно выполняемых заданий (-1 - задействованы все процессоры)\n",
    "        scoring=smape_score, # Передача функции ошибки через make_scorer в HalvingGridSearchCV\n",
    "        error_score='raise', #0 , # \n",
    "        random_state=STATE\n",
    "    )\n",
    "    grid.fit(features_train, target_train)\n",
    "    \n",
    "    finish_time = time.time()\n",
    "    funtion_time = finish_time - start_time\n",
    "    \n",
    "    return grid, funtion_time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Вывод на печать результатов модели\n",
    "def print_model_result(grids, data_times, model_name):\n",
    "    print('Модель   :', model_name)\n",
    "    print('RMSE     :', grids[-1].best_score_)\n",
    "    print(f'Время    : {data_times[-1]} секунд')\n",
    "    print('Параметры:\\n', grids[-1].best_estimator_)\n",
    "    print()\n",
    "    print('-'*20)\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Функции моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LinearRegression\n",
    "def grids_LinearRegression(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    grids, \n",
    "    data_times\n",
    "):\n",
    "    \n",
    "    grid, time_best = params_and_model_selection(\n",
    "        features_train, \n",
    "        features_test, \n",
    "        target_train, \n",
    "        target_test, \n",
    "        [{\n",
    "            'regressor': [LinearRegression()] # score: R^2\n",
    "        }]\n",
    "    )\n",
    "    \n",
    "    grids.append(grid)\n",
    "    data_times.append(time_best)\n",
    "    \n",
    "    return grids, data_times\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DecisionTreeRegressor\n",
    "def grids_DecisionTreeRegressor(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    grids, \n",
    "    data_times\n",
    "):\n",
    "    \n",
    "    grids_this = 0\n",
    "    grids_best = 0\n",
    "    funtion_time = 0\n",
    "    time_best = 0\n",
    "    \n",
    "    # Поиск \"regressor__max_depth\"\n",
    "    \n",
    "    range_min = 1\n",
    "    range_max = 201\n",
    "    range_step = 20\n",
    "    \n",
    "    for i in range(1, 5, 1):\n",
    "        # Поиск лучших параметров\n",
    "        grids_this, funtion_time = params_and_model_selection(\n",
    "            features_train, \n",
    "            features_test, \n",
    "            target_train, \n",
    "            target_test, \n",
    "            [{\n",
    "                'regressor': [DecisionTreeRegressor(random_state=STATE)], # score: R^2\n",
    "                'regressor__max_depth': range(\n",
    "                    range_min, \n",
    "                    range_max, \n",
    "                    range_step\n",
    "                )\n",
    "            }]\n",
    "        )\n",
    "        # Выбор лучшей модели\n",
    "        if grids_best == 0:\n",
    "            grids_best = grids_this\n",
    "            time_best = funtion_time\n",
    "        elif grids_this.best_score_ > grids_best.best_score_:\n",
    "            grids_best = grids_this\n",
    "            time_best = funtion_time\n",
    "        if range_step == 1: break\n",
    "        # Выбор параметров поиска\n",
    "        regressor__max_depth = grids_this.best_params_['regressor__max_depth']\n",
    "        if int(regressor__max_depth - range_step / 2) > 0:\n",
    "            range_min = int(regressor__max_depth - range_step / 2)\n",
    "        else:\n",
    "            range_min = regressor__max_depth\n",
    "        range_max = int(regressor__max_depth + range_step / 2) + 1\n",
    "        range_step = int(range_step / 2)\n",
    "    \n",
    "    grids.append(grids_best)\n",
    "    data_times.append(time_best)\n",
    "    \n",
    "    return grids, data_times\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RandomForestRegressor\n",
    "def grids_RandomForestRegressor(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    grids, \n",
    "    data_times\n",
    "):\n",
    "    \n",
    "    grids_this = 0\n",
    "    grids_best = 0\n",
    "    funtion_time = 0\n",
    "    time_best = 0\n",
    "    \n",
    "    # Поиск \"regressor__max_depth\"\n",
    "    \n",
    "    range_min = 20\n",
    "    range_max = 61\n",
    "    range_step = 20\n",
    "    \n",
    "    for i in range(1, 5, 1):\n",
    "        \n",
    "        # Поиск лучших параметров\n",
    "        grids_this, funtion_time = params_and_model_selection(\n",
    "            features_train, \n",
    "            features_test, \n",
    "            target_train, \n",
    "            target_test, \n",
    "            [{\n",
    "                'regressor': [RandomForestRegressor(random_state=STATE)], # score: R^2\n",
    "                'regressor__max_depth': range(\n",
    "                    range_min, \n",
    "                    range_max, \n",
    "                    range_step\n",
    "                ), \n",
    "                'regressor__n_estimators': [1]\n",
    "            }]\n",
    "        )\n",
    "        # Выбор лучшей модели\n",
    "        if grids_best == 0:\n",
    "            grids_best = grids_this\n",
    "            time_best = funtion_time\n",
    "        elif grids_this.best_score_ > grids_best.best_score_: \n",
    "            grids_best = grids_this\n",
    "            time_best = funtion_time\n",
    "        if range_step == 1: break\n",
    "        # Выбор параметров поиска\n",
    "        regressor__max_depth = grids_this.best_params_['regressor__max_depth']\n",
    "        if int(regressor__max_depth - range_step / 2) > 0:\n",
    "            range_min = int(regressor__max_depth - range_step / 2)\n",
    "        else:\n",
    "            range_min = regressor__max_depth\n",
    "        range_max = int(regressor__max_depth + range_step / 2) + 1\n",
    "        range_step = int(range_step / 2)\n",
    "        if range_step == 0: range_step = 1\n",
    "    \n",
    "    # Поиск \"regressor__n_estimators\"\n",
    "    \n",
    "    range_min = 10\n",
    "    range_max = 31\n",
    "    range_step = 10\n",
    "    \n",
    "    for i in range(1, 5, 1):\n",
    "        \n",
    "        # Поиск лучших параметров\n",
    "        grids_this, funtion_time = params_and_model_selection(\n",
    "            features_train, \n",
    "            features_test, \n",
    "            target_train, \n",
    "            target_test, \n",
    "            [{\n",
    "                'regressor': [RandomForestRegressor(random_state=STATE)], # score: R^2\n",
    "                'regressor__max_depth': [regressor__max_depth], \n",
    "                'regressor__n_estimators': range(\n",
    "                    range_min, \n",
    "                    range_max, \n",
    "                    range_step\n",
    "                )\n",
    "            }]\n",
    "        )\n",
    "        # Выбор лучшей модели\n",
    "        if grids_best == 0: \n",
    "            grids_best = grids_this\n",
    "            time_best = funtion_time\n",
    "        elif grids_this.best_score_ > grids_best.best_score_: \n",
    "            grids_best = grids_this\n",
    "            time_best = funtion_time\n",
    "        if range_step == 1: break\n",
    "        # Выбор параметров поиска\n",
    "        regressor__n_estimators = grids_this.best_params_['regressor__n_estimators']\n",
    "        if int(regressor__n_estimators - range_step / 2) > 0:\n",
    "            range_min = int(regressor__n_estimators - range_step / 2)\n",
    "        else:\n",
    "            range_min = regressor__n_estimators\n",
    "        range_max = int(regressor__n_estimators + range_step / 2) + 1\n",
    "        range_step = int(range_step / 10)\n",
    "        if range_step == 0: range_step = 1\n",
    "    \n",
    "    grids.append(grids_best)\n",
    "    data_times.append(time_best)\n",
    "    \n",
    "    return grids, data_times\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SGDRegressor\n",
    "def grids_SGDRegressor(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    grids, \n",
    "    data_times\n",
    "):\n",
    "    \n",
    "    # Поиск лучших параметров\n",
    "    \n",
    "    grids_best, time_best = params_and_model_selection(\n",
    "        features_train, \n",
    "        features_test, \n",
    "        target_train, \n",
    "        target_test, \n",
    "        [{\n",
    "            'regressor': [SGDRegressor()]\n",
    "        }]\n",
    "    )\n",
    "    \n",
    "    grids.append(grids_best)\n",
    "    data_times.append(time_best)\n",
    "    \n",
    "    return grids, data_times\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MLPRegressor\n",
    "def grids_MLPRegressor(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    grids, \n",
    "    data_times\n",
    "):\n",
    "    \n",
    "    # Поиск лучших параметров\n",
    "    \n",
    "    grids_best, time_best = params_and_model_selection(\n",
    "        features_train, \n",
    "        features_test, \n",
    "        target_train, \n",
    "        target_test, \n",
    "        [{\n",
    "            'regressor': [MLPRegressor()]\n",
    "        }]\n",
    "    )\n",
    "    \n",
    "    grids.append(grids_best)\n",
    "    data_times.append(time_best)\n",
    "    \n",
    "    return grids, data_times\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CatBoostRegressor\n",
    "def grids_CatBoostRegressor(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    grids, \n",
    "    data_times\n",
    "):\n",
    "    \n",
    "    \n",
    "    grids_best, time_best = params_and_model_selection(\n",
    "        features_train, \n",
    "        features_test, \n",
    "        target_train, \n",
    "        target_test, \n",
    "        [{\n",
    "            'regressor': [CatBoostRegressor()]\n",
    "        }]\n",
    "    )\n",
    "    \n",
    "    \n",
    "    grids.append(grids_best)\n",
    "    data_times.append(time_best)\n",
    "    \n",
    "    return grids, data_times\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LGBMRegressor\n",
    "def grids_LGBMRegressor(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    grids, \n",
    "    data_times\n",
    "):\n",
    "    \n",
    "    # Поиск лучших параметров\n",
    "    \n",
    "    grids_this = 0\n",
    "    grids_best = 0\n",
    "    funtion_time = 0\n",
    "    time_best = 0\n",
    "    \n",
    "    # Поиск \"regressor__max_depth\"\n",
    "    \n",
    "    range_min = 1\n",
    "    range_max = 201\n",
    "    range_step = 20\n",
    "    \n",
    "    for i in range(1, 5, 1):\n",
    "        \n",
    "        #print('regressor__max_depth =', range(range_min, range_max, range_step))\n",
    "        \n",
    "        # Поиск лучших параметров\n",
    "        grids_this, funtion_time = params_and_model_selection(\n",
    "            features_train, \n",
    "            features_test, \n",
    "            target_train, \n",
    "            target_test, \n",
    "            [{\n",
    "                'regressor': [LGBMRegressor(random_state=STATE)], # score: R^2\n",
    "                'regressor__max_depth': range(\n",
    "                    range_min, \n",
    "                    range_max, \n",
    "                    range_step\n",
    "                ), \n",
    "                'regressor__n_estimators': [1]\n",
    "            }]\n",
    "        )\n",
    "        # Выбор лучшей модели\n",
    "        if grids_best == 0: \n",
    "            grids_best = grids_this\n",
    "            time_best = funtion_time\n",
    "        elif grids_this.best_score_ > grids_best.best_score_: \n",
    "            grids_best = grids_this\n",
    "            time_best = funtion_time\n",
    "        if range_step == 1: break\n",
    "        # Выбор параметров поиска\n",
    "        regressor__max_depth = grids_this.best_params_['regressor__max_depth']\n",
    "        if int(regressor__max_depth - range_step / 2) > 0:\n",
    "            range_min = int(regressor__max_depth - range_step / 2)\n",
    "        else:\n",
    "            range_min = regressor__max_depth\n",
    "        range_max = int(regressor__max_depth + range_step / 2) + 1\n",
    "        range_step = int(range_step / 2)\n",
    "        if range_step == 0: range_step = 1\n",
    "    \n",
    "    # Поиск \"regressor__n_estimators\"\n",
    "    \n",
    "    range_min = 1\n",
    "    range_max = 51\n",
    "    range_step = 10\n",
    "    \n",
    "    for i in range(1, 5, 1):\n",
    "        \n",
    "        #print('regressor__n_estimators =', range(range_min, range_max, range_step))\n",
    "        \n",
    "        # Поиск лучших параметров\n",
    "        grids_this, funtion_time = params_and_model_selection(\n",
    "            features_train, \n",
    "            features_test, \n",
    "            target_train, \n",
    "            target_test, \n",
    "            [{\n",
    "                'regressor': [LGBMRegressor(random_state=STATE)], # score: R^2\n",
    "                'regressor__max_depth': [regressor__max_depth], \n",
    "                'regressor__n_estimators': range(\n",
    "                    range_min, \n",
    "                    range_max, \n",
    "                    range_step\n",
    "                )\n",
    "            }]\n",
    "        )\n",
    "        # Выбор лучшей модели\n",
    "        if grids_best == 0: \n",
    "            grids_best = grids_this\n",
    "            time_best = funtion_time\n",
    "        elif grids_this.best_score_ > grids_best.best_score_: \n",
    "            grids_best = grids_this\n",
    "            time_best = funtion_time\n",
    "        if range_step == 1: break\n",
    "        # Выбор параметров поиска\n",
    "        regressor__n_estimators = grids_this.best_params_['regressor__n_estimators']\n",
    "        if int(regressor__n_estimators - range_step / 2) > 0:\n",
    "            range_min = int(regressor__n_estimators - range_step / 2)\n",
    "        else:\n",
    "            range_min = regressor__n_estimators\n",
    "        range_max = int(regressor__n_estimators + range_step / 2) + 1\n",
    "        range_step = int(range_step / 10)\n",
    "        if range_step == 0: range_step = 1\n",
    "    \n",
    "    grids.append(grids_best)\n",
    "    data_times.append(time_best)\n",
    "    \n",
    "    return grids, data_times\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Применение функций"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 247630 entries, 223244 to 257691\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   vehicle_type       247630 non-null  float64\n",
      " 1   registration_year  247630 non-null  int64  \n",
      " 2   gearbox            247630 non-null  float64\n",
      " 3   power              247630 non-null  float64\n",
      " 4   model              247630 non-null  float64\n",
      " 5   kilometer          247630 non-null  int64  \n",
      " 6   fuel_type          247630 non-null  float64\n",
      " 7   brand              247630 non-null  float64\n",
      " 8   repaired           247630 non-null  float64\n",
      "dtypes: float64(7), int64(2)\n",
      "memory usage: 18.9 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vehicle_type</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>repaired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>223244</th>\n",
       "      <td>4759.405926</td>\n",
       "      <td>2005</td>\n",
       "      <td>6967.682167</td>\n",
       "      <td>190.0</td>\n",
       "      <td>5767.135775</td>\n",
       "      <td>150000</td>\n",
       "      <td>6756.042179</td>\n",
       "      <td>6010.171569</td>\n",
       "      <td>5385.242357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98910</th>\n",
       "      <td>3540.618365</td>\n",
       "      <td>2000</td>\n",
       "      <td>2270.272120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3793.928198</td>\n",
       "      <td>150000</td>\n",
       "      <td>3394.617204</td>\n",
       "      <td>3261.835992</td>\n",
       "      <td>2672.054560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62348</th>\n",
       "      <td>6827.486197</td>\n",
       "      <td>2010</td>\n",
       "      <td>4081.965033</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3223.449143</td>\n",
       "      <td>80000</td>\n",
       "      <td>3807.829533</td>\n",
       "      <td>3147.120726</td>\n",
       "      <td>5385.242357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318127</th>\n",
       "      <td>4759.405926</td>\n",
       "      <td>2009</td>\n",
       "      <td>4081.965033</td>\n",
       "      <td>143.0</td>\n",
       "      <td>5890.414408</td>\n",
       "      <td>125000</td>\n",
       "      <td>6756.042179</td>\n",
       "      <td>6384.934049</td>\n",
       "      <td>5385.242357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290228</th>\n",
       "      <td>4759.405926</td>\n",
       "      <td>1993</td>\n",
       "      <td>4081.965033</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2604.409468</td>\n",
       "      <td>150000</td>\n",
       "      <td>3394.617204</td>\n",
       "      <td>4530.331913</td>\n",
       "      <td>5385.242357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        vehicle_type  registration_year      gearbox  power        model  \\\n",
       "223244   4759.405926               2005  6967.682167  190.0  5767.135775   \n",
       "98910    3540.618365               2000  2270.272120    0.0  3793.928198   \n",
       "62348    6827.486197               2010  4081.965033  120.0  3223.449143   \n",
       "318127   4759.405926               2009  4081.965033  143.0  5890.414408   \n",
       "290228   4759.405926               1993  4081.965033   45.0  2604.409468   \n",
       "\n",
       "        kilometer    fuel_type        brand     repaired  \n",
       "223244     150000  6756.042179  6010.171569  5385.242357  \n",
       "98910      150000  3394.617204  3261.835992  2672.054560  \n",
       "62348       80000  3807.829533  3147.120726  5385.242357  \n",
       "318127     125000  6756.042179  6384.934049  5385.242357  \n",
       "290228     150000  3394.617204  4530.331913  5385.242357  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Подготовка выборок из датафрейма\n",
    "\n",
    "# Разделение обучающего датафрейма на целевую и нецелевую выборку\n",
    "features_train,  features_test, target_train, target_test = data_preprocessing(data, 'price')\n",
    "\n",
    "# Кодирование категориальных текстовых признаков\n",
    "# с помощью технологии TargetEncoder \n",
    "features_encoding = ['vehicle_type', 'gearbox', 'model', 'fuel_type', 'brand', 'repaired']\n",
    "te_fit = TargetEncoder().fit(features_train[features_encoding], target_train)\n",
    "features_train[features_encoding] = te_fit.transform(features_train[features_encoding])\n",
    "features_test[features_encoding] = te_fit.transform(features_test[features_encoding])\n",
    "\n",
    "print(features_train.info())\n",
    "features_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Поиск лучших моделей и их параметров\n",
    "data_grids = []\n",
    "data_times = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Модель   : LinearRegression\n",
      "RMSE     : 3069.23279496429\n",
      "Время    : 2.045431137084961 секунд\n",
      "Параметры:\n",
      " Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(transformers=[('pipeline',\n",
      "                                                  Pipeline(steps=[('standardscaler',\n",
      "                                                                   StandardScaler())]),\n",
      "                                                  Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',\n",
      "       'kilometer', 'fuel_type', 'brand', 'repaired'],\n",
      "      dtype='object'))])),\n",
      "                ('regressor', LinearRegression())])\n",
      "\n",
      "--------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# LinearRegression (dummy-model)\n",
    "data_grids, data_times = grids_LinearRegression(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    data_grids, \n",
    "    data_times\n",
    ")\n",
    "print_model_result(data_grids, data_times, 'LinearRegression')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Модель   : DecisionTreeRegressor\n",
      "RMSE     : 3520.5951256556477\n",
      "Время    : 14.969941139221191 секунд\n",
      "Параметры:\n",
      " Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(transformers=[('pipeline',\n",
      "                                                  Pipeline(steps=[('standardscaler',\n",
      "                                                                   StandardScaler())]),\n",
      "                                                  Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',\n",
      "       'kilometer', 'fuel_type', 'brand', 'repaired'],\n",
      "      dtype='object'))])),\n",
      "                ('regressor',\n",
      "                 DecisionTreeRegressor(max_depth=1, random_state=42))])\n",
      "\n",
      "--------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DecisionTreeRegressor\n",
    "data_grids, data_times = grids_DecisionTreeRegressor(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    data_grids, \n",
    "    data_times\n",
    ")\n",
    "print_model_result(data_grids, data_times, 'DecisionTreeRegressor')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Модель   : SGDRegressor\n",
      "RMSE     : 3089.779746424787\n",
      "Время    : 5.435046195983887 секунд\n",
      "Параметры:\n",
      " Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(transformers=[('pipeline',\n",
      "                                                  Pipeline(steps=[('standardscaler',\n",
      "                                                                   StandardScaler())]),\n",
      "                                                  Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',\n",
      "       'kilometer', 'fuel_type', 'brand', 'repaired'],\n",
      "      dtype='object'))])),\n",
      "                ('regressor', SGDRegressor())])\n",
      "\n",
      "--------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# SGDRegressor\n",
    "data_grids, data_times = grids_SGDRegressor(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    data_grids, \n",
    "    data_times\n",
    ")\n",
    "print_model_result(data_grids, data_times, 'SGDRegressor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.097814\n",
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      "999:\tlearn: 1644.4227966\ttotal: 38.6s\tremaining: 0us\n",
      "Модель   : CatBoostRegressor\n",
      "RMSE     : 1726.4647502068021\n",
      "Время    : 204.62451720237732 секунд\n",
      "Параметры:\n",
      " Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(transformers=[('pipeline',\n",
      "                                                  Pipeline(steps=[('standardscaler',\n",
      "                                                                   StandardScaler())]),\n",
      "                                                  Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',\n",
      "       'kilometer', 'fuel_type', 'brand', 'repaired'],\n",
      "      dtype='object'))])),\n",
      "                ('regressor',\n",
      "                 <catboost.core.CatBoostRegressor object at 0x7fa94bf40310>)])\n",
      "\n",
      "--------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# CatBoostRegressor\n",
    "data_grids, data_times = grids_CatBoostRegressor(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    data_grids, \n",
    "    data_times\n",
    ")\n",
    "print_model_result(data_grids, data_times, 'CatBoostRegressor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Модель   : LGBMRegressor\n",
      "RMSE     : 4384.374489966158\n",
      "Время    : 322.27989077568054 секунд\n",
      "Параметры:\n",
      " Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(transformers=[('pipeline',\n",
      "                                                  Pipeline(steps=[('standardscaler',\n",
      "                                                                   StandardScaler())]),\n",
      "                                                  Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',\n",
      "       'kilometer', 'fuel_type', 'brand', 'repaired'],\n",
      "      dtype='object'))])),\n",
      "                ('regressor',\n",
      "                 LGBMRegressor(max_depth=1, n_estimators=1, random_state=42))])\n",
      "\n",
      "--------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# LGBMRegressor\n",
    "data_grids, data_times = grids_LGBMRegressor(\n",
    "    features_train, \n",
    "    features_test, \n",
    "    target_train, \n",
    "    target_test, \n",
    "    data_grids, \n",
    "    data_times\n",
    ")\n",
    "print_model_result(data_grids, data_times, 'LGBMRegressor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выбор лучшей модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшее время :  204.62451720237732\n",
      "Лучшее RMSE  :  1726.4647502068021\n",
      "Лучшая модель: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HalvingGridSearchCV(cv=4, error_score='raise',\n",
       "                    estimator=Pipeline(steps=[('preprocessor',\n",
       "                                               ColumnTransformer(transformers=[('pipeline',\n",
       "                                                                                Pipeline(steps=[('standardscaler',\n",
       "                                                                                                 StandardScaler())]),\n",
       "                                                                                Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',\n",
       "       'kilometer', 'fuel_type', 'brand', 'repaired'],\n",
       "      dtype='object'))])),\n",
       "                                              ('regressor',\n",
       "                                               <catboost.core.CatBoostRegressor object at 0x7fa950219220>)]),\n",
       "                    n_jobs=-1,\n",
       "                    param_grid=[{'regressor': [<catboost.core.CatBoostRegressor object at 0x7fa950219220>]}],\n",
       "                    random_state=42,\n",
       "                    refit=<function _refit_callable at 0x7fa95cd84f70>,\n",
       "                    scoring=make_scorer(mean_squared_error, squared=False))"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Лучшая модель из расчета RMSE\n",
    "data_grids_best = data_grids[0]\n",
    "data_times_best = data_times[0]\n",
    "n = 0\n",
    "for i in range(0, len(data_grids)-1):\n",
    "    if data_grids[i].best_score_ < data_grids_best.best_score_: \n",
    "    #if (data_grids[i].best_score_ < data_grids_best.best_score_) & (data_times[i] < data_times_best): \n",
    "        data_grids_best = data_grids[i]\n",
    "        data_times_best = data_times[i]\n",
    "\n",
    "print('Лучшее время : ', data_times_best)\n",
    "print('Лучшее RMSE  : ', data_grids_best.best_score_)\n",
    "print('Лучшая модель: ')\n",
    "data_grids_best\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE = 1709.3459181142257\n",
      "Время предсказания = 0.3003842830657959 секунд\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "# Предсказание лучшей модели\n",
    "predict = data_grids_best.predict(features_test)\n",
    "\n",
    "finish_time = time.time()\n",
    "funtion_time = finish_time - start_time\n",
    "\n",
    "# Расчет RMSE и времени выполнения предсказания\n",
    "print('RMSE =', mean_squared_error(target_test, predict, squared=False))\n",
    "print(f'Время предсказания = {funtion_time} секунд')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Анализ моделей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для использования в данном проекте были выбраны следующие модели: `LinearRegression` в качестве дамми-модели, `DecisionTreeRegressor`, `SGDRegressor`, `CatBoostRegressor` от Яндекса и `LGBMRegressor`. \n",
    "\n",
    "По критерию минимального параметра RMSE в качестве лучшей модели после обучения была выбрана модель `CatBoostRegressor` с параметрами по-умолчанию. Ее показатели на обучающей выборке:\n",
    "\n",
    "**RMSE** : 1726.4647502068021<br>\n",
    "**Время**: 178.93473863601685 секунд<br>\n",
    "\n",
    "При предсказании на тестовых данных эта модель показала хорошие результаты:\n",
    "\n",
    "**RMSE** : 1709.3459181142257<br>\n",
    "**Время**: 0.128037691116333 секунд<br>\n",
    "\n",
    "RMSE оказался близким к тому, что было получено при обучении, а время предсказания многократно меньше обучения. Учитывая тот факт, что заказчика интересуют время обучения и предсказания, но отсутствуют точные критерии требуемого времени, а RMSE выбранной модели, как и требуется, меньше 2500, то для эксплуатации предлагается модель `CatBoostRegressor` с параметрами по-умолчанию."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Выводы проекта"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Цель проекта достигнута. Выбрана модель `CatBoostRegressor()` с параметрами по-умолчанию для предсказания цены подержанных автомобилей по их параметрам. Для этого были выполнены следующие действия:\n",
    "1. Загружены и проканализированы данные.\n",
    "1. Выполнена предварительная обработка данных.\n",
    "1. Данные подготовлены к машинному обучению.\n",
    "1. Обучены несколько моделей, включая  `LGBMRegressor` и одна не бустинговая модель.\n",
    "1. Выбрана лучшая модель по критериям заказчика, включая минимальное время обучения и предсказания, а также RMSE меньшее 2500.\n",
    "1. Проанализированы результаты обучения и предсказания и сделаны выводы."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Чек-лист проверки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поставьте 'x' в выполненных пунктах. Далее нажмите Shift+Enter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  Jupyter Notebook открыт\n",
    "- [x]  Весь код выполняется без ошибок\n",
    "- [x]  Ячейки с кодом расположены в порядке исполнения\n",
    "- [x]  Выполнена загрузка и подготовка данных\n",
    "- [x]  Выполнено обучение моделей\n",
    "- [x]  Есть анализ скорости работы и качества моделей"
   ]
  }
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